experimental investigation 9

Scientific Reasoning - Planning Comparative and Experimental Investigations

Comparative vs. experimental investigations, variables (independent and dependent) and group (control and experimental), formulating hypothesis.

Copy and paste the link code above.

Related Items

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism, run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bevans, R. (2022, December 05). A Quick Guide to Experimental Design | 5 Steps & Examples. Scribbr. Retrieved 19 August 2024, from https://www.scribbr.co.uk/research-methods/guide-to-experimental-design/

Is this article helpful?

Rebecca Bevans

• My Storyboards

Exploring the Art of Experimental Design: A Step-by-Step Guide for Students and Educators

Experimental design for students.

Experimental design is a key method used in subjects like biology, chemistry, physics, psychology, and social sciences. It helps us figure out how different factors affect what we're studying, whether it's plants, chemicals, physical laws, human behavior, or how society works. Basically, it's a way to set up experiments so we can test ideas, see what happens, and make sense of our results. It's super important for students and researchers who want to answer big questions in science and understand the world better. Experimental design skills can be applied in situations ranging from problem solving to data analysis; they are wide reaching and can frequently be applied outside the classroom. The teaching of these skills is a very important part of science education, but is often overlooked when focused on teaching the content. As science educators, we have all seen the benefits practical work has for student engagement and understanding. However, with the time constraints placed on the curriculum, the time needed for students to develop these experimental research design and investigative skills can get squeezed out. Too often they get a ‘recipe’ to follow, which doesn’t allow them to take ownership of their practical work. From a very young age, they start to think about the world around them. They ask questions then use observations and evidence to answer them. Students tend to have intelligent, interesting, and testable questions that they love to ask. As educators, we should be working towards encouraging these questions and in turn, nurturing this natural curiosity in the world around them.

Teaching the design of experiments and letting students develop their own questions and hypotheses takes time. These materials have been created to scaffold and structure the process to allow teachers to focus on improving the key ideas in experimental design. Allowing students to ask their own questions, write their own hypotheses, and plan and carry out their own investigations is a valuable experience for them. This will lead to students having more ownership of their work. When students carry out the experimental method for their own questions, they reflect on how scientists have historically come to understand how the universe works.

Take a look at the printer-friendly pages and worksheet templates below!

What are the Steps of Experimental Design?

Embarking on the journey of scientific discovery begins with mastering experimental design steps. This foundational process is essential for formulating experiments that yield reliable and insightful results, guiding researchers and students alike through the detailed planning, experimental research design, and execution of their studies. By leveraging an experimental design template, participants can ensure the integrity and validity of their findings. Whether it's through designing a scientific experiment or engaging in experimental design activities, the aim is to foster a deep understanding of the fundamentals: How should experiments be designed? What are the 7 experimental design steps? How can you design your own experiment?

This is an exploration of the seven key experimental method steps, experimental design ideas, and ways to integrate design of experiments. Student projects can benefit greatly from supplemental worksheets and we will also provide resources such as worksheets aimed at teaching experimental design effectively. Let’s dive into the essential stages that underpin the process of designing an experiment, equipping learners with the tools to explore their scientific curiosity.

1. Question

This is a key part of the scientific method and the experimental design process. Students enjoy coming up with questions. Formulating questions is a deep and meaningful activity that can give students ownership over their work. A great way of getting students to think of how to visualize their research question is using a mind map storyboard.

Ask students to think of any questions they want to answer about the universe or get them to think about questions they have about a particular topic. All questions are good questions, but some are easier to test than others.

2. Hypothesis

A hypothesis is known as an educated guess. A hypothesis should be a statement that can be tested scientifically. At the end of the experiment, look back to see whether the conclusion supports the hypothesis or not.

Forming good hypotheses can be challenging for students to grasp. It is important to remember that the hypothesis is not a research question, it is a testable statement . One way of forming a hypothesis is to form it as an “if... then...” statement. This certainly isn't the only or best way to form a hypothesis, but can be a very easy formula for students to use when first starting out.

An “if... then...” statement requires students to identify the variables first, and that may change the order in which they complete the stages of the visual organizer. After identifying the dependent and independent variables, the hypothesis then takes the form if [change in independent variable], then [change in dependent variable].

For example, if an experiment were looking for the effect of caffeine on reaction time, the independent variable would be amount of caffeine and the dependent variable would be reaction time. The “if, then” hypothesis could be: If you increase the amount of caffeine taken, then the reaction time will decrease.

3. Explanation of Hypothesis

What led you to this hypothesis? What is the scientific background behind your hypothesis? Depending on age and ability, students use their prior knowledge to explain why they have chosen their hypotheses, or alternatively, research using books or the internet. This could also be a good time to discuss with students what a reliable source is.

For example, students may reference previous studies showing the alertness effects of caffeine to explain why they hypothesize caffeine intake will reduce reaction time.

4. Prediction

The prediction is slightly different to the hypothesis. A hypothesis is a testable statement, whereas the prediction is more specific to the experiment. In the discovery of the structure of DNA, the hypothesis proposed that DNA has a helical structure. The prediction was that the X-ray diffraction pattern of DNA would be an X shape.

Students should formulate a prediction that is a specific, measurable outcome based on their hypothesis. Rather than just stating "caffeine will decrease reaction time," students could predict that "drinking 2 cans of soda (90mg caffeine) will reduce average reaction time by 50 milliseconds compared to drinking no caffeine."

5. Identification of Variables

Below is an example of a Discussion Storyboard that can be used to get your students talking about variables in experimental design.

The three types of variables you will need to discuss with your students are dependent, independent, and controlled variables. To keep this simple, refer to these as "what you are going to measure", "what you are going to change", and "what you are going to keep the same". With more advanced students, you should encourage them to use the correct vocabulary.

Dependent variables are what is measured or observed by the scientist. These measurements will often be repeated because repeated measurements makes your data more reliable.

The independent variables are variables that scientists decide to change to see what effect it has on the dependent variable. Only one is chosen because it would be difficult to figure out which variable is causing any change you observe.

Controlled variables are quantities or factors that scientists want to remain the same throughout the experiment. They are controlled to remain constant, so as to not affect the dependent variable. Controlling these allows scientists to see how the independent variable affects the dependent variable within the experimental group.

Use this example below in your lessons, or delete the answers and set it as an activity for students to complete on Storyboard That.

6. Risk Assessment

Ultimately this must be signed off on by a responsible adult, but it is important to get students to think about how they will keep themselves safe. In this part, students should identify potential risks and then explain how they are going to minimize risk. An activity to help students develop these skills is to get them to identify and manage risks in different situations. Using the storyboard below, get students to complete the second column of the T-chart by saying, "What is risk?", then explaining how they could manage that risk. This storyboard could also be projected for a class discussion.

7. Materials

In this section, students will list the materials they need for the experiments, including any safety equipment that they have highlighted as needing in the risk assessment section. This is a great time to talk to students about choosing tools that are suitable for the job. You are going to use a different tool to measure the width of a hair than to measure the width of a football field!

8. General Plan and Diagram

It is important to talk to students about reproducibility. They should write a procedure that would allow their experimental method to be reproduced easily by another scientist. The easiest and most concise way for students to do this is by making a numbered list of instructions. A useful activity here could be getting students to explain how to make a cup of tea or a sandwich. Act out the process, pointing out any steps they’ve missed.

For English Language Learners and students who struggle with written English, students can describe the steps in their experiment visually using Storyboard That.

Not every experiment will need a diagram, but some plans will be greatly improved by including one. Have students focus on producing clear and easy-to-understand diagrams that illustrate the experimental group.

For example, a procedure to test the effect of sunlight on plant growth utilizing completely randomized design could detail:

• Select 10 similar seedlings of the same age and variety
• Prepare 2 identical trays with the same soil mixture
• Place 5 plants in each tray; label one set "sunlight" and one set "shade"
• Position sunlight tray by a south-facing window, and shade tray in a dark closet
• Water both trays with 50 mL water every 2 days
• After 3 weeks, remove plants and measure heights in cm

9. Carry Out Experiment

Once their procedure is approved, students should carefully carry out their planned experiment, following their written instructions. As data is collected, students should organize the raw results in tables, graphs, photos or drawings. This creates clear documentation for analyzing trends.

Some best practices for data collection include:

• Record quantitative data numerically with units
• Note qualitative observations with detailed descriptions
• Capture set up through illustrations or photos
• Write observations of unexpected events
• Identify data outliers and sources of error

For example, in the plant growth experiment, students could record:

They would also describe observations like leaf color change or directional bending visually or in writing.

It is crucial that students practice safe science procedures. Adult supervision is required for experimentation, along with proper risk assessment.

Well-documented data collection allows for deeper analysis after experiment completion to determine whether hypotheses and predictions were supported.

Completed Examples

Resources and Experimental Design Examples

Using visual organizers is an effective way to get your students working as scientists in the classroom.

There are many ways to use these investigation planning tools to scaffold and structure students' work while they are working as scientists. Students can complete the planning stage on Storyboard That using the text boxes and diagrams, or you could print them off and have students complete them by hand. Another great way to use them is to project the planning sheet onto an interactive whiteboard and work through how to complete the planning materials as a group. Project it onto a screen and have students write their answers on sticky notes and put their ideas in the correct section of the planning document.

Very young learners can still start to think as scientists! They have loads of questions about the world around them and you can start to make a note of these in a mind map. Sometimes you can even start to ‘investigate’ these questions through play.

The foundation resource is intended for elementary students or students who need more support. It is designed to follow exactly the same process as the higher resources, but made slightly easier. The key difference between the two resources are the details that students are required to think about and the technical vocabulary used. For example, it is important that students identify variables when they are designing their investigations. In the higher version, students not only have to identify the variables, but make other comments, such as how they are going to measure the dependent variable or utilizing completely randomized design. As well as the difference in scaffolding between the two levels of resources, you may want to further differentiate by how the learners are supported by teachers and assistants in the room.

Students could also be encouraged to make their experimental plan easier to understand by using graphics, and this could also be used to support ELLs.

Students need to be assessed on their science inquiry skills alongside the assessment of their knowledge. Not only will that let students focus on developing their skills, but will also allow them to use their assessment information in a way that will help them improve their science skills. Using Quick Rubric , you can create a quick and easy assessment framework and share it with students so they know how to succeed at every stage. As well as providing formative assessment which will drive learning, this can also be used to assess student work at the end of an investigation and set targets for when they next attempt to plan their own investigation. The rubrics have been written in a way to allow students to access them easily. This way they can be shared with students as they are working through the planning process so students know what a good experimental design looks like.

Printable Resources

Return to top

Related Activities

Additional Worksheets

If you're looking to add additional projects or continue to customize worksheets, take a look at several template pages we've compiled for you below. Each worksheet can be copied and tailored to your projects or students! Students can also be encouraged to create their own if they want to try organizing information in an easy to understand way.

• Lab Worksheets
• Discussion Worksheets
• Checklist Worksheets

Related Resources

• Scientific Method Steps
• Science Discussion Storyboards
• Developing Critical Thinking Skills

How to Teach Students the Design of Experiments

Encourage questioning and curiosity.

Foster a culture of inquiry by encouraging students to ask questions about the world around them.

Formulate testable hypotheses

Teach students how to develop hypotheses that can be scientifically tested. Help them understand the difference between a hypothesis and a question.

Provide scientific background

Help students understand the scientific principles and concepts relevant to their hypotheses. Encourage them to draw on prior knowledge or conduct research to support their hypotheses.

Identify variables

Teach students about the three types of variables (dependent, independent, and controlled) and how they relate to experimental design. Emphasize the importance of controlling variables and measuring the dependent variable accurately.

Plan and diagram the experiment

Guide students in developing a clear and reproducible experimental procedure. Encourage them to create a step-by-step plan or use visual diagrams to illustrate the process.

Carry out the experiment and analyze data

Support students as they conduct the experiment according to their plan. Guide them in collecting data in a meaningful and organized manner. Assist them in analyzing the data and drawing conclusions based on their findings.

Frequently Asked Questions about Experimental Design for Students

What are some common experimental design tools and techniques that students can use.

Common experimental design tools and techniques that students can use include random assignment, control groups, blinding, replication, and statistical analysis. Students can also use observational studies, surveys, and experiments with natural or quasi-experimental designs. They can also use data visualization tools to analyze and present their results.

How can experimental design help students develop critical thinking skills?

Experimental design helps students develop critical thinking skills by encouraging them to think systematically and logically about scientific problems. It requires students to analyze data, identify patterns, and draw conclusions based on evidence. It also helps students to develop problem-solving skills by providing opportunities to design and conduct experiments to test hypotheses.

How can experimental design be used to address real-world problems?

Experimental design can be used to address real-world problems by identifying variables that contribute to a particular problem and testing interventions to see if they are effective in addressing the problem. For example, experimental design can be used to test the effectiveness of new medical treatments or to evaluate the impact of social interventions on reducing poverty or improving educational outcomes.

What are some common experimental design pitfalls that students should avoid?

Common experimental design pitfalls that students should avoid include failing to control variables, using biased samples, relying on anecdotal evidence, and failing to measure dependent variables accurately. Students should also be aware of ethical considerations when conducting experiments, such as obtaining informed consent and protecting the privacy of research subjects.

• 353/365 ~ Second Fall #running #injury • Ray Bouknight • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Always Writing • mrsdkrebs • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Batteries • Razor512 • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Bleed for It • zerojay • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Bulbs • Roo Reynolds • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Change • dominiccampbell • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Children • Quang Minh (YILKA) • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Danger • KatJaTo • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• draw • Asja. • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Epic Fireworks Safety Goggles • EpicFireworks • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• GERMAN BUNSEN • jasonwoodhead23 • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Heart Dissection • tjmwatson • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• ISST 2014 Munich • romanboed • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Lightbulb! • Matthew Wynn • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Mini magnifying glass • SkintDad.co.uk • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Plants • henna lion • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Plants • Graham S Dean Photography • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Pré Treino.... São Carlos está foda com essa queimada toda #asma #athsma #ashmatt #asthma • .v1ctor Casale. • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• puzzle • olgaberrios • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Puzzled • Brad Montgomery • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Question Mark • ryanmilani • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Radiator • Conal Gallagher • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Red Tool Box • marinetank0 • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Remote Control • Sean MacEntee • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• stopwatch • Search Engine People Blog • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Thinking • Caramdir • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Thumb Update: The hot-glue induced burn now has a purple blister. Purple is my favorite color. (September 26, 2012 at 04:16PM) • elisharene • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• Washing my Hands 2 • AlishaV • License Attribution (http://creativecommons.org/licenses/by/2.0/)
• Windows • Stanley Zimny (Thank You for 18 Million views) • License Attribution, Non Commercial (http://creativecommons.org/licenses/by-nc/2.0/)
• wire • Dyroc • License Attribution (http://creativecommons.org/licenses/by/2.0/)

Pricing for Schools & Districts

Limited Time

• 10 Teachers for One Year
• 2 Hours of Virtual PD

30 Day Money Back Guarantee • New Customers Only • Full Price After Introductory Offer • Access is for 1 Calendar Year

• 30 Day Money Back Guarantee
• New Customers Only
• Full Price After Introductory Offer

Limited Time. New Customers Only

Back to school special!

Purchase orders must be received by 9/6/24.

30 Day Money Back Guarantee. New Customers Only. Full Price After Introductory Offer. Access is for 1 Calendar Year

Generating a Quote

This is usually pretty quick :)

Quote Sent!

Email Sent to

• Schools & departments

Chapter 9. Experimental design

‘Experimental design’ is a huge topic, with many books devoted to the topic. The vast majority of experiments in the biological sciences, however, are based on a few foundational principles. We focus on these principles in this (and following) chapter(s) to provide the resources to design reliable, replicable and powerful experiments.

That said, our current discussion of experimental design only tangentially addresses some fundamental topics.  For example, we provide thorough discussion of power analysis for a variety of experimental designs (see later Chapters) to help researchers decide upon the appropriate level of replication (sample size) for their experiment. 

However, we provide little discussion of why experiments require replication, generally (although we elude to this in our discussion of sampling error in earlier chapters).  Likewise, we currently do not discuss how to select appropriate controls for an experiment (and when controls are necessary). 

Therefore, our treatment of experimental design assumes some prior knowledge.  We refer readers to Ruxton & Colegrave’s excellent book, “Experimental Design for the Life Sciences” for further support; as mentioned elsewhere, this book is written to be accessible to undergraduates, but I know faculty members who find this book for themselves. 

Our treatment of experimental design addresses recommendations in recent literature with respect to designing experiments that use animal models.  That said, our content applies to experiments in biology, generally (e.g., physiology, ecology, evolution), beyond the use of animal models.

View examples of this here:

We focus on the following topics:

• Use of randomization.  ‘Randomization’ serves to eliminate bias, and is the most essential assumption in statistical tests.  We discuss not only the allocation subjects to treatments (the most common use of randomization), but also implementing randomization in all aspects of an experiment.
• Selecting materials (subjects) for an experiment to maximize an experiment’s ability to yield generalizable conclusions.  Note that ecologists and evolutionary biologists should, where applicable, also consider sampling methods for field research (i.e., done ‘in the wild’), which we do not discuss here.
• The importance of ‘blinding’ throughout an experiment; studies that lack blinding can yield surprisingly biased results.
• ‘Blocking’ as a means of accounting for unwanted variation in an experiment.  We introduce the techniques to analyze experiments with ‘block’ in Chapters that address analyses of multiple explanatory variables (i.e., Chapters, “Analyzing experiments with more than 1 factor”, “Mixed effects models”, and “Understanding covariates:  …”).
• ‘Covariates’ as a way of dealing with, and understanding additional sources of variation in an experiment.  We address techniques to analyze experiments with covariates in the Chapter, “Understanding covariates:  …”.

Experimental Design: Randomization (Part 1)

This video introduces the need for randomization in all aspects of an experiment.

This video can be watched in two parts:

0:00 - 46:59   (47 minutes) Randomizing subjects to treatments

47:00 - 1:14:39 (28 minutes)  Randomizing other aspects of experiments

Experimental Design: Randomization (Part 2)

This video discusses the need for samples that are representative of a population of interest.  In particular, we discuss some severe consequences for society that have arisen due to experiments that use unrepresentative samples.  We emphasize randomization as a component of sampling.

Experimental Design: Blinding

Here, we discuss how 'blinding' can reduce bias.

Experimental Design: Blocking

This video discusses "blocking" as a method to control unwanted variation in experiments and thereby increase experimental power.

Experimental Design: Covariates

In this video, we discuss how including 'covariates' in a data analysis can facilitate understanding of differences between treatment groups.  We discuss what 'covariates' are, how they help, and the kinds of biological insight they can provide when their measurement is included in an experimental design and analysis.

NOTE that this video begins with a review of concepts discussed in the 'Blocking' video.  You can skip this introduction by starting the video at 4:50.

Recommended reading:

Ruxton & Colegrave: Experimental Design for the Life Sciences

Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

good and valuable

Very very good

Good presentation.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Publishing Research

10 Tips to Prevent Research Papers From Being Retracted

Research paper retractions represent a critical event in the scientific community. When a published article…

• Industry News

Google Releases 2024 Scholar Metrics, Evaluates Impact of Scholarly Articles

Google has released its 2024 Scholar Metrics, assessing scholarly articles from 2019 to 2023. This…

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• AI in Academia
• Promoting Research
• Career Corner
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer-Review Week 2023
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
• measuring variables to statistically control for them in your analyses.
• balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal,
• Study environment (e.g., temperature or lighting),
• Participant’s frequency of buying fast food,
• Participant’s familiarity with the specific fast food brand,
• Participant’s socioeconomic status.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• A control group that’s presented with red advertisements for a fast food meal.
• An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Prevent plagiarism. Run a free check.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is a Controlled Experiment? | Definitions & Examples. Scribbr. Retrieved August 19, 2024, from https://www.scribbr.com/methodology/controlled-experiment/

Is this article helpful?

Pritha Bhandari

Other students also liked, extraneous variables | examples, types & controls, guide to experimental design | overview, steps, & examples, how to write a lab report, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Your browser is not supported

Sorry but it looks as if your browser is out of date. To get the best experience using our site we recommend that you upgrade or switch browsers.

Find a solution

• Skip to main content
• Skip to navigation

• Back to parent navigation item
• Find resources

Experiments and investigations

• Cross-curricular activities
• Meet the scientists
• Boost your knowledge
• Beyond the classroom
• Get funding
• About the RSC

• More navigation items

Inspiration and resources for experiments and investigations

Primary science investigations

Air pressure, gases and the leaky bottle

Try this simple investigation to explore the effects of air pressure, with detailed teacher notes, classroom slides and a video demonstration.

Properties of gases, air pressure and ‘sticky’ cups

Try this investigation to explore the effect of heat on gases, with detailed teacher notes, classroom slides and a video demonstration.

Dissolving, density and sugary drinks

Irreversible changes and the ‘freaky hand’, irreversible changes and the ‘fire extinguisher’, properties of solids and ‘biscuit bashing’, from kitchen to classroom.

Simple chemistry experiments using kitchen cupboard equipment.

Chalky spinach

How does the food we eat affect our bodies? Discover the acid inside spinach, and why it can feel chalky

How to purify water

Explore the water cycle, and see how it can be used to purify your own water. A perfect experiment for the classroom, or at home, with kit list and safety instructions included.

Investigating surface tension with milk

Kitchen roll chromatography, how to make butter, demonstrations.

Separating mixtures | Primary science video demonstrations

Fun demonstrations of different mixtures, including racing liquids and rainbow colours.

Solids | Primary science video demonstrations

Display some exciting solid properties to primary learners: including biscuit bashing, dissolving and heavy sugar.

Gases | Primary science video demonstrations

Showing the properties of gases to primary learners, with examples from: the leaky bottle and the sticky cups

Developing scientific skills

Help your pupils develop their scientific skills with these experiments and resources

Asking scientific questions

Pollutants produced by chemical changes | 9–11 years

Connect your curriculum teaching on chemical changes to engaging sustainability contexts. This topic web suggests classroom activities linked to plastic degradation and clean cooking. 

Electricity and batteries | 7–9 years

Connect your curriculum teaching on electricity to engaging sustainability contexts. This topic web suggests classroom activities linked to batteries and electric cars. 

Electricity production and use | 9–11 years

Connect your curriculum teaching on electricity to engaging sustainability contexts. This topic web suggests classroom activities linked to sustainable sources of electricity and monitoring our electricity usage. 

Science concept cartoons: rusting

In association with Millgate House Education

Use this conversation starter to get your pupils thinking about the process of rusting, and discussing different possibilities.

Science Concept Cartoons: Acid rain

Spark discussion and stimulate thinking about acid rain in a way that encourages pupils to share their ideas

Science concept cartoons: condensation

Show this concept cartoon to your class to get them talking and thinking about condensation in new ways.

Observing and measuring

An Dàrna Cogadh: lìontan nam beachdan saidheans

Lìon nam beachdan mollaichte airson ceangal a dhèanamh eadar saidheans agus cuspair An Dàrna Cogadh. Faodaidh tu eachdraidh agus saidheans ionnsachadh còmhla le gnìomhachasan airson buidhnean de dh’aoisean diofraichte

Bendy bones

What makes bones strong? Children can learn about the role that calcium plays in skeletons

Biodiversity and habitats | 4–7 years

Connect your curriculum teaching on habitats to engaging sustainability contexts. This topic web suggests classroom activities linked to increasing biodiversity and protecting marine habitats.

Changing materials | Primary science video demonstrations

Material changes are demonstrated using fun examples: bouncy custard, bath bombs, burning candles, fire extinguishers, lava lamps and many more.

Colourful food: kitchen science podcasts

Introduce your students to colours in food with this short podcast.

Decaying and materials in mobiles | 9–11 years

Connect your curriculum teaching on materials to engaging sustainability contexts. This topic web suggests classroom activities linked to the materials found in mobile phones and how different materials decay.

Making predictions

Protecting animals and their habitats | 7–9 years

Connect your curriculum teaching on habitats to engaging sustainability contexts. This topic web suggests classroom activities linked to protecting the habitats of orangutans and polar bears. 

Try this investigation to get learners exploring the mass of sugar dissolved in their favourite drinks, with detailed teacher notes, classroom slides and a video demonstration.

Freezing and the ‘intriguing ice’ experiment

Try this investigation to explore how materials change when they freeze, with detailed teacher notes, classroom slides and a video demonstration.

Red cabbage rainbows

In this activity, learners create rainbows using homemade red cabbage indicator paper. Includes video aimed at learners, kit list, instruction and explanation

Insulation investigation

Which material makes the warmest jacket? Investigate the insulating properties of various materials with this activity for ages 7–14

Recording data

Chemistry in sport

Our very first global experiment compares the performance enhancement of student-made sports drinks vs water

Designing experiments

Materials, recycling and rubbish | 4–7 years

Connect your curriculum teaching on materials to engaging sustainability contexts. This topic web suggests classroom activities linked to recycling and how much rubbish is produced in your school.

Try this investigation to explore what materials need to burn, with detailed teacher notes, classroom slides and a video demonstration.

Interpreting data

The life of water

Get hands on with H 2 O, changing states of matter and the water cycle. These experiments and investigations involve water in the context of space

Popular collections

Global experiments.

Engage your students with hands-on, fully-resourced, group activities that are adaptable for all ages

Edible experiments

Discover the importance of chemistry in everyday eating experiences with this collection of edible experiments. 

That's Chemistry!

That’s Chemistry!

Discover the informative chapters of That’s Chemistry! Each chapter explains a chemistry concept and gives numerous ideas for activites to support students’ learning.

• Newsletters
• Find your local education coordinator

Site powered by Webvision Cloud

Experimental and Numerical Investigation on Scour Pits of Granular Flow Downstream Check Dam

• Research paper
• Published: 16 August 2024

Cite this article

• Yunyun Fan 1 ,
• Huixian Wang 1 &
• Fang Zhang 2  

13 Accesses

Explore all metrics

In this study, experimental and numerical investigation were conducted on scour pits of granular flow downstream. Firstly, the flow flume experiment studied the influence of the material mass from the source area, the initial location of the material, the height of the check dam, and the length of the entrainment area. In the experiment a wedge was used to simulate the check dam. Then a numerical research of typical experiment processes was carried out using the discrete element method. The experiment results show that apparent entrainment only occurs under specific conditions of high initial energy, low wedge height, and small length of entrainment area. Based on the numerical verification, the numerical results obtained by the discrete element method were used for analysis, which show that the formation of the scour pit undergoes three main sub-stages, including downstream impact entrainment at the initial sub-stage, retrospective scraper entrainment toward the upstream at the middle sub-stage, and shear deformation zone formation in the entrainment area by friction and pushing at the later sub-stage. The damage caused by shear deformation in the entrainment area deserves special attention, as this area is difficult to observe directly. However, it can influence the stability of scour pits and surrounding structures. The results of this study may provide the basis for further research on the scour pits.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Comparison of Scour and Flow Characteristics Around Circular and Oblong Bridge Piers in Seepage Affected Alluvial Channels

Characteristics of gully bed scour and siltation between check dams

Numerical investigation of mining pit effects on maximum scour depth around bridge pier with different shape

Data availability.

The test and calculation data used to support the findings of this study are available from the corresponding author upon request.

Pan HL, Yang S, Ou GQ, Huang JC (2013) Local scour and the laws of scour pit’s shape downstream of debris flow sabo dam. J Mt Sci 10(6):1063–1073

Article   Google Scholar  

Liu DC, You Y, Liu JF, Mu Q, Feng J, Zhang L, Wei AH, Tan HF (2022) Stability analysis of check dam impacted by intermittent surge. Bull Eng Geol Env 81(1):53

Liu SL, You Y, Zhang GZ, Wang D, Zhao HX, Sun H (2016) Calculation of the ultimate depth of a scour pit after debris flow through drainage canal ribs. J Mt Sci 13(2):246–254

Chen HY, Cui P, Chen JP, Tang JB (2016) Effects of spillway types on debris flow trajectory and scour behind a sabo dam. J Mt Sci 13(2):203–212

Chen JY, Hsu HH, Hong YM (2016) The influence of upstream slope on the local scour at drop structure. J Mt Sci 13(12):2237–2248

Jalili Kashtiban Y, Saeidi A, Farinas MI, Quirion M (2021) A review on existing methods to assess hydraulic erodibility downstream of dam spillways. Water 13(22):3205

Chen H, Liu J, Zhao W (2016) Effects of Y-type spillway lateral contraction ratios on debris-flow patterns and scour features downriver of a check dam. Nat Hazard Earth Syst Sci 16(11):2433–2442

Yang Q, Yu P, Liu H (2021) CFD modelling of local scour around Tri-USAF in sand with different arrangements under steady current. Ocean Eng 235:109359

Gautam S, Dutta D, Bihs H, Afzal MS (2021) Three-dimensional computational fluid dynamics modelling of scour around a single pile due to combined action of the waves and current using level-set method. Coast Eng 170:104002

Cantero-Chinchilla FN, de Almeida GAM, Manes C (2021) Temporal evolution of clear-water local scour at bridge piers with flow-dependent debris accumulations. J Hydraul Eng 147(10):06021013

Pan HL, Huang JC, Ou GQ (2015) Mechanism of downcutting erosion of debris flow over a movable bed. J Mt Sci 12(1):243–250

Haas T, Woerkom T (2016) Bed scour by debris flows: experimental investigation of effects of debris-flow composition. Earth Surf Proc Land 41(13):1951–1966

Yang DX, You Y, Zhao WY, Huang H, Sun H, Liu Y (2020) Abrasion behavior and anti-wear measures of debris flow drainage channel with large gradient. Water 12(7):1868

Iverson RM, Ouyang C (2015) Entrainment of bed material by earth-surface mass flows: review and reformulation of depthintegrated theory. Rev Geophys 53(1):27–58

Trujillo-Vela MG, Ramos-Cañón AM, Escobar-Vargas JA, Galindo-Torres SA (2022) An overview of debris-flow mathematical modelling. Earth Sci Rev 232:104135

Chen HY, Ruan HC, Chen JA, Li X, Yu YH (2022) Review of investigations on hazard chains triggered by river-blocking debris flows and dam-break floods. Front Earth Sci 10:830044

Wang X, Chen JG, Chen HY, Chen XQ, Li S, Zhao WY (2022) Erosion process of multiple debris flow surges caused by check dam removal: an experimental study. Water Resour Res 58(3):e2021WR030688

Li P, Wang JD, Hu KH, Qiu HJ, Xie JL (2022) Experimental investigation on debris flow resistance and entrainment characteristics: effects of the erodible bed with discontinuous grading. J Mt Sci 19(8):2397–2419

Huo M, Yang XG, Zhou HW, Liang YF, Zhou JW (2019) Entrainment effects and the dynamical evolution of debris avalanche/flow on substrate materials. J Mt Sci 16(8):1760–1773

Fan Y, Wu F, Li M, Liang L (2019) Research on the entrainment of path material by the granular flow. KSCE J Civ Eng 23(12):5051–5063

Pan HL, Wang R, Huang JC, Ou GQ (2013) Study on the ultimate depth of scour pit downstream of debris flow sabo dam based on the energy method. Eng Geol 160:103–109

Goodwin GR, Choi CE, Yune CY (2021) Translational inertial effects and scaling considerations for coarse granular flows impacting landslide-resisting barriers. J Geotech Geoenv Eng 147(12):04021153

Fan Y, Su S, Zhang F, Wu F (2023) Numerical investigation on the process of obstructing granular flow by multi-layer rigid netting barriers. Granular Matter 25:74

Jiang YJ, Towhata I (2013) Experimental study of dry granular flow and impact behavior against a rigid retaining wall. Rock Mech Rock Eng 46(4):713–729

van der Vaart K, Gajjar P, Epely-Chauvin G, Andreini N, Gray JMNT, Ancey C (2015) Underlying asymmetry within particle size segregation. Phys Rev Lett 114(23):238001

Itasca Consulting Group Inc PFC (2014) Particle flow code ver 5.0 manual. Itasca, Minneapolis

Jiang M, Shen Z, Wang J (2015) A novel three-dimensional contact model for granulates incorporating rolling and twisting resistances. Comput Geotech 65:147–163

Zhao S, Evans TM, Zhou X (2018) Shear-induced anisotropy of granular materials with rolling resistance and particle shape effects. Int J Solids Struct 150:268–281

Ai J, Chen JF, Rotter JM, Ooi JY (2011) Assessment of rolling resistance models in discrete element simulations. Powder Technol 206(3):269–282

Download references

Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities of China (Grant No. N2201021), the National Key Research and Development Program of China (Grant No. 2017YFC1503101), the National Natural Science Foundation of China (Grant No. 41201007), and the Research Fund for General Science Project of Department of Education of Liaoning Province (Grant No. L2013103).

Author information

Authors and affiliations.

School of Resource and Civil Engineering, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, 110004, People’s Republic of China

Yunyun Fan & Huixian Wang

Shenyang Branch of Lanzhou Engineering & Research Institute of Nonferrous Metallurgy Co., Ltd, No. 3A-1 Yuanhang Middle Road, Hunnan District, Shenyang, 110180, People’s Republic of China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yunyun Fan .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Fan, Y., Wang, H. & Zhang, F. Experimental and Numerical Investigation on Scour Pits of Granular Flow Downstream Check Dam. Int J Civ Eng (2024). https://doi.org/10.1007/s40999-024-01005-9

Download citation

Received : 20 March 2023

Revised : 04 June 2024

Accepted : 17 June 2024

Published : 16 August 2024

DOI : https://doi.org/10.1007/s40999-024-01005-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Granular flow
• Numerical simulation
• Find a journal
• Publish with us
• Track your research

LIVE 0m ago

Mother charged with murder of 10-year-old daughter on the Gold Coast

ABC Gold Coast

Topic: Crime

Sophie Wang was found dead at her Gold Coast home.  ( Supplied: Emmanuel College )

Police have charged a woman with murder after her 10-year-old daughter was found dead in a Gold Coast home.

Yingying Xu, 46, has been charged with the murder of her daughter Sophie Wang at their Emerald Lakes home on Tuesday.

Ms Xu did not appear when her matter was mentioned at the Southport Magistrates Court on Wednesday morning.

The ABC understands Sophie had several visible wounds, including a cut to her neck.

"Yesterday police were called to a residence … following a report of a young girl being discovered unresponsive by her father," Detective Acting Inspector Kent Ellis said.

"Despite her father and paramedic's best efforts, the young girl was pronounced deceased."

Ms Xu was arrested hours later on a nearby street.

Investigators remained at the scene on Wednesday morning. ( ABC News )

Sophie to be 'dearly missed' by school community

In a statement, Emmanuel College said Sophie, who started as a prep student in 2019 and was in grade 5, would be remembered for her "kindness, caring nature and unwavering positivity".

"She was known for her love and passion for reading and learning, and was a high-achieving student who excelled in academic pursuits," the statement said.

"Sophie's legacy at Emmanuel College is one of dedication, compassion, and enthusiasm."

The college said Sophie contributed significantly to co-curricular performing arts programs and enrichment activities.

Flowers left at the scene where a 10-year-old girl was allegedly murdered. ( ABC News: Alexandria Utting )

"Her valuable contributions to our community will be cherished and remembered by all who had the privilege of knowing her," she said.

"She will be dearly missed."

The college said her death had devastated their community.

"Our prayers are with the family, friends, first responders and all affected by the loss of this beloved child in her home," the school said.

The college said it had counselling and pastoral care teams available to provide "comprehensive support to students, staff and parents as our community comes to terms with this distressing news".

In a statement, a Griffith University spokesperson said they were "deeply saddened to hear about the tragic death of the daughter of a valued member of the Griffith community".

The ABC understands Sophie's father is Associate Professor Yun Wang.

"Our thoughts are with the family and friends at this incredibly difficult time," a spokesperson said. 

"The university has offered support to him and his close colleagues."

Live Moment

Look back at how ABC readers and other Australians responded to this live moment.

Detective praises quick arrest after triple-0 call

Jessica Black

Police are not looking for anyone else connected to the girl's death.

"I understand the concern among the community and it's a tragic event," Detective Acting Inspector Kent Ellis says. "I'll just remind them that this was resolved extremely quickly ... the response by police was excellent."

Only one triple-0 call was received, he says.

We'll end our live coverage there, thanks for following.

Detective Acting Inspector Kent Ellis says he's restricted in what he can say as it's before the court.

"Our investigation is ongoing and we are committed to ensuring justice is received for this 10-year-old victim," he says.

He believes the girl's father rang triple-0 shortly after 6pm

The scene was one of the most confronting Detective Acting Inspector Kent Ellis says he's ever seen.

"In my 15 years as a detective, it's one of the most confronting scenes that I've seen. "We understand that this is going to cause distress amongst the community but can I please remind the community that there is no one outstanding."

Suppression order unsuccessful

On Wednesday, Police Prosecutor Sergeant Nicole Jackson applied for Ms Xu's name and details of the case to be suppressed.

Lawyer Sophie Robertson — who appeared for several media outlets, including the ABC — argued the court did not have the power to grant a suppression order.

The police application was later withdrawn.

Ms Xu did not make a bail application as defendants charged with murder are unable to apply for bail in the lower courts and must make a separate application in the Queensland Supreme Court.

Speaking on Wednesday, nearby resident Chris Sydes said it was "heartbreaking" to hear what had happened.

He said he had seen the young girl in the street only days ago.

"I've been home all day and I haven't heard anything, I had my dogs dropped off at 6pm and all of a sudden police started showing up," Mr Sydes said. 

"I've since found out what's happened. It's just terrible news.

"It's devastating to hear that little girl [is dead]. It's heartbreaking, this shouldn't happen, it's just terrible."

Emergency services were called to Central Place at Emerald Lakes on Tuesday evening. ( ABC News )

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS. A lock ( Lock Locked padlock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Search Awards
• Recent Awards
• Presidential and Honorary Awards
• About Awards
• How to Manage Your Award
• Grant General Conditions
• Cooperative Agreement Conditions
• Special Conditions
• Federal Demonstration Partnership
• Policy Office Website

Please report errors in award information by writing to: [email protected] .

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Improving mixed-mode fracture properties of concrete reinforced with macrosynthetic plastic fibers: an experimental and numerical investigation.

1. Introduction

2. dataset materials and specimen preparation, 3. fracture modeling, 4. verification of numerical models, 4.1. shape form factor, 4.2. production of mesh fragmentation, 4.3. result of verification, 5. parametric study, 5.1. effect of mspf on the behavior of concrete in mode i fracture, 5.2. effect of fibers on the behavior of concrete in mode i and mode ii fractures, 5.3. investigation of fracture parameters under a combination of fracture modes, 6. conclusions.

• Effective stress intensity factor ( K eff ) was crucial for understanding the material’s response to mixed-mode fractures.
• As M e approaches zero and shear deformation becomes dominant, the resistance to mixed-mode fractures decreases.
• Adding macrosynthetic fibers significantly boosts mixed-mode fracture toughness, especially in mixed-mode I/II conditions (0.5 < M e < 0.9).
• There was an approximately 400% increase in mixed-mode fracture toughness with fiber content ( ρ eff = 1 signifies no fiber content).
• Optimal fiber content for enhancing concrete durability is around 4%; beyond this percentage, the increase in energy absorption plateaus due to diminished bond strength between fibers and cement mortar.
• A standardized method for presenting mixed-mode outcomes was developed, comparing stress intensity factors ( K II and K IIC ) with ( K I and K IC ).
• A power law criterion was established with specific values for p and q that align with numerical data on fractures.

7. Future Research

• Further exploration of the optimal distribution and fiber content for specific construction scenarios.
• Deeper understanding of the complex interactions between fibers and the cement matrix in concrete.

Author Contributions

Data availability statement, conflicts of interest, abbreviations.

• Oni, O.; Arum, C. Workability and Compressive Strength of Concrete Containing Binary Cement, Mixed Fines, and Superplasticizer. Facta Univ.-Ser. Archit. Civ. Eng. 2023 , 21 , 299–314. [ Google Scholar ] [ CrossRef ]
• Nedeljković, M.; Visser, J.; Šavija, B.; Valcke, S.; Schlangen, E. Use of Fine Recycled Concrete Aggregates in Concrete: A Critical Review. J. Build. Eng. 2021 , 38 , 102196. [ Google Scholar ] [ CrossRef ]
• Fang, M.; Chen, Y.; Deng, Y.; Wang, Z.; Zhu, M. Toughness Improvement Mechanism and Evaluation of Cement Concrete for Road Pavement: A Review. J. Road Eng. 2023 , 3 , 125–140. [ Google Scholar ] [ CrossRef ]
• Munawar, H.S.; Hammad, A.W.A.; Haddad, A.; Soares, C.A.P.; Waller, S.T. Image-Based Crack Detection Methods: A Review. Infrastructures 2021 , 6 , 115. [ Google Scholar ] [ CrossRef ]
• Nyathi, M.A.; Bai, J.; Wilson, I.D. Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms. Appl. Sci. 2023 , 13 , 4981. [ Google Scholar ] [ CrossRef ]
• Chen, J.; Xiong, F.; Zhu, Y.; Yan, H. A Crack Detection Method for Underwater Concrete Structures Using Sensing-Heating System with Porous Casing. Measurement 2021 , 168 , 108332. [ Google Scholar ] [ CrossRef ]
• Murthi, P.; Poongodi, K.; Awoyera, P.O.; Gobinath, R.; Saravanan, R. Enhancing the Strength Properties of High-Performance Concrete Using Ternary Blended Cement: OPC, Nano-Silica, Bagasse Ash. Silicon 2020 , 12 , 1949–1956. [ Google Scholar ] [ CrossRef ]
• Burhan, L.; Ghafor, K.; Mohammed, A. Enhancing the Fresh and Hardened Properties of the Early Age Concrete Modified with Powder Polymers and Characterized Using Different Models. Adv. Civ. Eng. Mater. 2020 , 9 , 227–249. [ Google Scholar ] [ CrossRef ]
• Gamal, H.A.; El-Feky, M.S.; Alharbi, Y.R.; Abadel, A.A.; Kohail, M. Enhancement of the Concrete Durability with Hybrid Nano Materials. Sustainability 2021 , 13 , 1373. [ Google Scholar ] [ CrossRef ]
• Yuhazri, M.Y.; Zulfikar, A.J.; Ginting, A. Fiber Reinforced Polymer Composite as a Strengthening of Concrete Structures: A Review. IOP Conf. Ser. Mater. Sci. Eng. 2020 , 1003 , 012135. [ Google Scholar ] [ CrossRef ]
• Zhou, H.; Jia, B.; Huang, H.; Mou, Y. Experimental Study on Basic Mechanical Properties of Basalt Fiber Reinforced Concrete. Materials 2020 , 13 , 1362. [ Google Scholar ] [ CrossRef ]
• Chen, Z.; Wang, X.; Jiang, K.; Zhao, X.; Liu, X.; Wu, Z. The Splitting Tensile Strength and Impact Resistance of Concrete Reinforced with Hybrid BFRP Minibars and Micro Fibers. J. Build. Eng. 2024 , 88 , 109188. [ Google Scholar ] [ CrossRef ]
• Dong, S.; Wang, D.; Wang, X.; D’Alessandro, A.; Ding, S.; Han, B.; Ou, J. Optimizing Flexural Cracking Process of Ultra-High Performance Concrete via Incorporating Microscale Steel Wires. Cem. Concr. Compos. 2022 , 134 , 104830. [ Google Scholar ] [ CrossRef ]
• Yang, L.; Xie, H.; Fang, S.; Huang, C.; Yang, A.; Chao, Y.J. Experimental Study on Mechanical Properties and Damage Mechanism of Basalt Fiber Reinforced Concrete under Uniaxial Compression. In Structures ; Elsevier: Amsterdam, The Netherlands, 2021; Volume 31, pp. 330–340. [ Google Scholar ]
• Latifi, M.R.; Biricik, Ö.; Mardani Aghabaglou, A. Effect of the Addition of Polypropylene Fiber on Concrete Properties. J. Adhes. Sci. Technol. 2022 , 36 , 345–369. [ Google Scholar ] [ CrossRef ]
• Yuan, Z.; Jia, Y. Mechanical Properties and Microstructure of Glass Fiber and Polypropylene Fiber Reinforced Concrete: An Experimental Study. Constr. Build. Mater. 2021 , 266 , 121048. [ Google Scholar ] [ CrossRef ]
• Shabbir, F.; Khan, Q.U.Z.; Rashid, M.U.; Muslim, A.; Raza, A. The Efficiency of Hybrid Fibers (Steel and Carbon Fibers) in Controlling Micro Cracking of Concrete Beams. Iran. J. Sci. Technol.-Trans. Civ. Eng. 2023 , 47 , 1425–1445. [ Google Scholar ] [ CrossRef ]
• Xu, H.; Wang, Z.; Shao, Z.; Cai, L.; Jin, H.; Zhang, Z.; Qiu, Z.; Rui, X.; Chen, T. Experimental Study on Durability of Fiber Reinforced Concrete: Effect of Cellulose Fiber, Polyvinyl Alcohol Fiber and Polyolefin Fiber. Constr. Build. Mater. 2021 , 306 , 124867. [ Google Scholar ] [ CrossRef ]
• Zheng, Y.; Lv, X.; Hu, S.; Zhuo, J.; Wan, C.; Liu, J. Mechanical Properties and Durability of Steel Fiber Reinforced Concrete: A Review. J. Build. Eng. 2024 , 82 , 108025. [ Google Scholar ] [ CrossRef ]
• Cheng, L.; Chen, S.; Chen, F.; Wang, C.; Chen, Q. Research Progress and Performance Evaluation of Polyvinyl Alcohol Fiber Engineered Cementitious Composites. Sustainability 2023 , 15 , 10991. [ Google Scholar ] [ CrossRef ]
• Jang, S.J.; Park, W.S.; Kim, S.W.; Kim, D.H.; Wang, Q.; Jeong, W.J.; Jin, A.H.; Yun, H. Do Effect of Contents, Tensile Strengths and Aspect Ratios of Hooked-End Steel Fibers (SFs) on Compressive and Flexural Performance of Normal Strength Concrete. Int. J. Concr. Struct. Mater. 2023 , 17 , 46. [ Google Scholar ] [ CrossRef ]
• Niu, X.J.; Zhao, Q.X.; Nie, Y. Effect of Polypropylene Macro-Fiber on Properties of High-Strength Concrete at Elevated Temperatures. Key Eng. Mater. 2015 , 629–630 , 284–290. [ Google Scholar ] [ CrossRef ]
• Gencel, O.; Yavuz Bayraktar, O.; Kaplan, G.; Benli, A.; Martínez-Barrera, G.; Brostow, W.; Tek, M.; Bodur, B. Characteristics of Hemp Fibre Reinforced Foam Concretes with Fly Ash and Taguchi Optimization. Constr. Build. Mater. 2021 , 294 , 123607. [ Google Scholar ] [ CrossRef ]
• Huang, J.; Qiu, S.; Rodrigue, D. Parameters Estimation and Fatigue Life Prediction of Sisal Fibre Reinforced Foam Concrete. J. Mater. Res. Technol. 2022 , 20 , 381–396. [ Google Scholar ] [ CrossRef ]
• Flores Nicolás, A.; Menchaca Campos, E.C.; Flores Nicolás, M.; Martínez González, J.J.; González Noriega, O.A.; Uruchurtu Chavarín, J. Influence of Recycled High-Density Polyethylene Fibers on the Mechanical and Electrochemical Properties of Reinforced Concrete. Fibers 2024 , 12 , 24. [ Google Scholar ] [ CrossRef ]
• Meena, A.; Surendranath, A.; Ramana, P.V. Assessment of Mechanical Properties and Workability for Polyethylene Terephthalate Fiber Reinforced Concrete. Mater. Today Proc. 2021 , 50 , 2307–2314. [ Google Scholar ] [ CrossRef ]
• Piryaei, M.; Komasi, M.; Hormozinejad, Y. Experimental Evaluation on Behavior of Poly Propylene Fiber-Reinforced Concrete Containing Poly Carboxylate Ether and E205 Additives. Constr. Build. Mater. 2022 , 347 , 128142. [ Google Scholar ] [ CrossRef ]
• Revathi, S.; Sathis Kumar, P.; Suresh, D.; Thowfick Anwar, S. Behaviour of Concrete with PET Bottles as Fibers & Silica Fume as Partial Replacement of Cement. Mater. Today Proc. 2023; in press. [ Google Scholar ] [ CrossRef ]
• Singh, K. Partial Replacement of Cement with Polyethylene Terephthalate Fiber to Study Its Effect on Various Properties of Concrete. Mater. Today Proc. 2020 , 37 , 3270–3274. [ Google Scholar ] [ CrossRef ]
• Ahmed, H.U.; Faraj, R.H.; Hilal, N.; Mohammed, A.A.; Sherwani, A.F.H. Use of Recycled Fibers in Concrete Composites: A Systematic Comprehensive Review. Compos. Part B Eng. 2021 , 215 , 108769. [ Google Scholar ] [ CrossRef ]
• Khatab, H.R.; Mohammed, S.J.; Hameed, L.A. Mechanical Properties of Concrete Contain Waste Fibers of Plastic Straps. IOP Conf. Ser. Mater. Sci. Eng. 2019 , 557 , 012059. [ Google Scholar ] [ CrossRef ]
• Mehvish, F.; Ahmed, A.; Saleem, M.M.; Saleem, M.A. Characterization of Concrete Incorporating Waste Polythene Bags Fibers. Pak. J. Engg. Appl. Sci 2020 , 26 , 93–101. [ Google Scholar ]
• Jain, A.; Siddique, S.; Gupta, T.; Jain, S.; Sharma, R.K.; Chaudhary, S. Evaluation of Concrete Containing Waste Plastic Shredded Fibers: Ductility Properties. Struct. Concr. 2021 , 22 , 566–575. [ Google Scholar ] [ CrossRef ]
• Ahmad, W.; Khan, M.; Smarzewski, P. Effect of Short Fiber Reinforcements on Fracture Performance of Cement-Based Materials: A Systematic Review Approach. Materials 2021 , 14 , 1745. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Voutetaki, M.E.; Naoum, M.C.; Papadopoulos, N.A.; Chalioris, C.E. Cracking Diagnosis in Fiber-Reinforced Concrete with Synthetic Fibers Using Piezoelectric Transducers. Fibers 2022 , 10 , 5. [ Google Scholar ] [ CrossRef ]
• Abou El-Mal, H.S.S.; Sherbini, A.S.; Sallam, H.E.M. Locating the Site of Diagonal Tension Crack Initiation and Path in Reinforced Concrete Beams. Ain Shams Eng. J. 2015 , 6 , 17–24. [ Google Scholar ] [ CrossRef ]
• Abed, F.; Sabbagh, M.K.; Karzad, A.S. Effect of Basalt Microfibers on the Shear Response of Short Concrete Beams Reinforced with BFRP Bars. Compos. Struct. 2021 , 269 , 114029. [ Google Scholar ] [ CrossRef ]
• Abed, F.; Alhafiz, A.R. Effect of Basalt Fibers on the Flexural Behavior of Concrete Beams Reinforced with BFRP Bars. Compos. Struct. 2019 , 215 , 23–34. [ Google Scholar ] [ CrossRef ]
• Chalioris, C.E.; Kosmidou, P.M.K.; Karayannis, C.G. Cyclic Response of Steel Fiber Reinforced Concrete Slender Beams: An Experimental Study. Materials 2019 , 12 , 1398. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, X.; Xing, G.; Niu, J.; Liu, B. Behavior of Plastic-Steel Fiber Reinforced Lightweight Aggregate Concrete Columns Subjected to Concentric Axial Loading. Int. J. Civ. Eng. 2021 , 19 , 283–300. [ Google Scholar ] [ CrossRef ]
• Yoo, D.Y.; Kang, S.T.; Yoon, Y.S. Effect of Fiber Length and Placement Method on Flexural Behavior, Tension-Softening Curve, and Fiber Distribution Characteristics of UHPFRC. Constr. Build. Mater. 2014 , 64 , 67–81. [ Google Scholar ] [ CrossRef ]
• Shen, M.; Huang, W.; Liu, J.; Zhou, Z. Axial Compressive Behavior of Rubberized Concrete-Filled Steel Tube Short Columns. Case Stud. Constr. Mater. 2022 , 16 , e00851. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Dai, Q.; Si, R.; Guo, S. Mechanical, Durability, and Microstructural Properties of Macro Synthetic Polypropylene (PP) Fiber-Reinforced Rubber Concrete. J. Clean. Prod. 2019 , 234 , 1351–1364. [ Google Scholar ] [ CrossRef ]
• Babafemi, A.J.; Boshoff, W.P. Tensile Creep of Macro-Synthetic Fibre Reinforced Concrete (MSFRC) under Uni-Axial Tensile Loading. Cem. Concr. Compos. 2015 , 55 , 62–69. [ Google Scholar ] [ CrossRef ]
• Yang, Z.; Ren, W.; Sharma, R.; McDonald, S.; Mostafavi, M.; Vertyagina, Y.; Marrow, T.J. In-Situ X-Ray Computed Tomography Characterisation of 3D Fracture Evolution and Image-Based Numerical Homogenisation of Concrete. Cem. Concr. Compos. 2017 , 75 , 74–83. [ Google Scholar ] [ CrossRef ]
• Dubey, S.; Ray, S. Influence of Heterogeneity on Size Effect Models and Fracture Characteristics of Plain Concrete. Theor. Appl. Fract. Mech. 2023 , 127 , 103993. [ Google Scholar ] [ CrossRef ]
• Yu, K.; Yang, Z.; Li, H.; Tat Ooi, E.; Li, S.; Liu, G.H. A Mesoscale Modelling Approach Coupling SBFEM, Continuous Damage Phase-Field Model and Discrete Cohesive Crack Model for Concrete Fracture. Eng. Fract. Mech. 2023 , 278 , 109030. [ Google Scholar ] [ CrossRef ]
• Zhu, S.; Zhou, Z.; Xiong, Y. Mesoscale Fracture Analysis of Three-Point Bending Concrete Beams Based on Cohesive Zone Model. Eng. Fract. Mech. 2024 , 296 , 109828. [ Google Scholar ] [ CrossRef ]
• Thakur, M.M.; Henningsson, A.; Engqvist, J.; Pierre, O.A.; Wright, J.; Hurley, R. On Mesoscale Modeling of Concrete: Role of Heterogeneities on Local Stresses, Strains, and Representative Volume Element. SSRN Electron. J. 2022 , 163 , 107031. [ Google Scholar ] [ CrossRef ]
• Wei, D.; Hurley, R.C.; Poh, L.H.; Dias-da-Costa, D.; Gan, Y. The Role of Particle Morphology on Concrete Fracture Behaviour: A Meso-Scale Modelling Approach. Cem. Concr. Res. 2020 , 134 , 106096. [ Google Scholar ] [ CrossRef ]
• Permanoon, A.; Akhaveissy, A. Failure of Existing Structures with Semi-Brittle Mechanical Properties on Meso Scale and Reduction of Computational Cost Using Non-Linear Topology Optimization. Constr. Build. Mater. 2022 , 319 , 126071. [ Google Scholar ] [ CrossRef ]
• Rahbar, E.; Permanoon, A.; Akhaveissy, A.H. Numerical Evaluation of Masonry-Infill Frames: Analysis of Lateral Strength and Failure Modes on Meso Scale. Structures 2023 , 52 , 779–793. [ Google Scholar ] [ CrossRef ]
• Kim, J.S.; Chung, S.Y.; Stephan, D.; Han, T.S. Issues on Characterization of Cement Paste Microstructures from μ-CT and Virtual Experiment Framework for Evaluating Mechanical Properties. Constr. Build. Mater. 2019 , 202 , 82–102. [ Google Scholar ] [ CrossRef ]
• Li, D.; Han, C.; Jin, L.; Du, X. An Atomistic Based Continuum Level 3D Mode-I Meso-Fracture Criterion for Cement-Based Concrete. Theor. Appl. Fract. Mech. 2023 , 124 , 103796. [ Google Scholar ] [ CrossRef ]
• Chodkowski, P.; Bobiński, J.; Tejchman, J. Limits of Enhanced of Macro- and Meso-Scale Continuum Models for Studying Size Effect in Concrete under Tension. Eur. J. Environ. Civ. Eng. 2022 , 26 , 5465–5495. [ Google Scholar ] [ CrossRef ]
• Permanoon, A.; Akhaveissy, A.H. Effects of Meso-Scale Modeling on Concrete Fracture Parameters Calculation. Period. Polytech. Civ. Eng. 2019 , 63 , 782–794. [ Google Scholar ] [ CrossRef ]
• Fattahi, M.; Malekshahi, M.; Permanoon, A. Mesoscale Numerical Modeling of Unreinforced Masonry Wall Response to Underground Dynamic Loads: A Comparative Study Utilizing FEM and DEM. Structures 2024 , 63 , 106460. [ Google Scholar ] [ CrossRef ]
• Akhaveissy, A.H.; Permanoon, A. An Investigation of Meso-Scale Crack Propagation Process in Concrete Beams Using Topology Optimization. Amirkabir J. Civ. Eng 2022 , 53 , 5281–5306. [ Google Scholar ]
• Pulatsu, B.; Erdogmus, E.; Lourenço, P.B.; Lemos, J.V.; Tuncay, K. Numerical Modeling of the Tension Stiffening in Reinforced Concrete Members via Discontinuum Models. Comput. Part. Mech. 2021 , 8 , 423–436. [ Google Scholar ] [ CrossRef ]
• Nitka, M.; Tejchman, J. Meso-Mechanical Modelling of Damage in Concrete Using Discrete Element Method with Porous ITZs of Defined Width around Aggregates. Eng. Fract. Mech. 2020 , 231 , 107029. [ Google Scholar ] [ CrossRef ]
• Xia, X.; Chen, F.; Gu, X.; Fang, N.; Zhang, Q. Interfacial Debonding Constitutive Model and XFEM Simulation for Mesoscale Concrete. Comput. Struct. 2021 , 242 , 106373. [ Google Scholar ] [ CrossRef ]
• Marzec, I.; Bobiński, J. Quantitative Assessment of the Influence of Tensile Softening of Concrete in Beams under Bending by Numerical Simulations with XFEM and Cohesive Cracks. Materials 2022 , 15 , 626. [ Google Scholar ] [ CrossRef ]
• Azúa-González, C.X. Multiscale Fracture Modelling of Concrete Using a Variational Micromechanics-Enriched Embedded Strong Discontinuity Finite Element Method. Ph.D. Thesis, Cardiff University, Cardiff, UK, 2022. [ Google Scholar ]
• Marković, N.; Grdić, D.; Stojković, N.; Topličić-Ćurčić, G.; Živković, D. Two-Dimensional Damage Localization Using a Piezoelectric Smart Aggregate Approach—Implementation on Arbitrary Shaped Concrete Plates. Materials 2024 , 17 , 218. [ Google Scholar ] [ CrossRef ]
• Hosseinzadehfard, E.; Mobaraki, B. Investigating Concrete Durability: The Impact of Natural Pozzolan as a Partial Substitute for Microsilica in Concrete Mixtures. Constr. Build. Mater. 2024 , 419 , 135491. [ Google Scholar ] [ CrossRef ]
• Mobaraki, B.; Ma, H.; Lozano Galant, J.A.; Turmo, J. Structural Health Monitoring of 2D Plane Structures. Appl. Sci. 2021 , 11 , 2000. [ Google Scholar ] [ CrossRef ]
• Sun, L.; Wang, C.; Zhang, C.; Yang, Z.; Li, C.; Qiao, P. Experimental Investigation on the Bond Performance of Sea Sand Coral Concrete with FRP Bar Reinforcement for Marine Environments. Adv. Struct. Eng. 2023 , 26 , 533–546. [ Google Scholar ] [ CrossRef ]
• Huang, H.; Yuan, Y.; Zhang, W.; Zhu, L. Property Assessment of High-Performance Concrete Containing Three Types of Fibers. Int. J. Concr. Struct. Mater. 2021 , 15 , 39. [ Google Scholar ] [ CrossRef ]
• Pang, B.; Jin, Z.; Zhang, Y.; Xu, L.; Li, M.; Wang, C.; Zhang, Y.; Yang, Y.; Zhao, P.; Bi, J.; et al. Ultraductile Waterborne Epoxy-Concrete Composite Repair Material: Epoxy-Fiber Synergistic Effect on Flexural and Tensile Performance. Cem. Concr. Compos. 2022 , 129 , 104463. [ Google Scholar ] [ CrossRef ]
• Asif, U.; Javed, M.F.; Abuhussain, M.; Ali, M.; Khan, W.A.; Mohamed, A. Predicting the Mechanical Properties of Plastic Concrete: An Optimization Method by Using Genetic Programming and Ensemble Learners. Case Stud. Constr. Mater. 2024 , 20 , e03135. [ Google Scholar ] [ CrossRef ]
• Khodaparasti, M.; Alijamaat, A.; Pouraminian, M. Prediction of the Concrete Compressive Strength Using Improved Random Forest Algorithm. J. Build. Pathol. Rehabil. 2023 , 8 , 92. [ Google Scholar ] [ CrossRef ]
• Bisheh, H. Design and Analysis of Hybrid Natural/Synthetic Fibre-Reinforced Composite Automotive Drive Shafts. Structures 2024 , 61 , 106057. [ Google Scholar ] [ CrossRef ]
• Camille, C.; Kahagala Hewage, D.; Mirza, O.; Mashiri, F.; Kirkland, B.; Clarke, T. Performance Behaviour of Macro-Synthetic Fibre Reinforced Concrete Subjected to Static and Dynamic Loadings for Sleeper Applications. Constr. Build. Mater. 2021 , 270 , 121469. [ Google Scholar ] [ CrossRef ]
• Camille, C.; Mirza, O.; Kirkland, B.; Clarke, T. Numerical Investigation of Prestressed Concrete Sleepers Reinforced with High-Performance Macro Synthetic Fibres. Eng. Fail. Anal. 2024 , 159 , 108149. [ Google Scholar ] [ CrossRef ]
• Ali, L.; El Ouni, M.H.; Raza, A. Tests of Polypropylene Macro Synthetic Fibers and GFRP Reinforced Concrete Columns Subjected to Concentric and Eccentric Loading. J. Build. Eng. 2021 , 43 , 103100. [ Google Scholar ] [ CrossRef ]
• Tan, H.; Huang, Y.; Liu, C.; Geubelle, P.H. The Mori-Tanaka Method for Composite Materials with Nonlinear Interface Debonding. Int. J. Plast. 2005 , 21 , 1890–1918. [ Google Scholar ] [ CrossRef ]
• Menetrey, P. Numerical Analysis of Punching Failure in Reinforced Concrete Structures ; EPFL: Lausanne, Switzerland, 1994. [ Google Scholar ]
• Wang, L.; Zeng, X.; Li, Y.; Yang, H.; Tang, S. Influences of MgO and PVA Fiber on the Abrasion and Cracking Resistance, Pore Structure and Fractal Features of Hydraulic Concrete. Fractal Fract. 2022 , 6 , 674. [ Google Scholar ] [ CrossRef ]
• Grassl, P.; Jirásek, M. Meso-Scale Approach to Modelling the Fracture Process Zone of Concrete Subjected to Uniaxial Tension. Int. J. Solids Struct. 2010 , 47 , 957–968. [ Google Scholar ] [ CrossRef ]
• Alfano, G.; Crisfield, M.A. Finite Element Interface Models for the Delamination Analysis of Laminated Composites: Mechanical and Computational Issues. Int. J. Numer. Methods Eng. 2001 , 50 , 1701–1736. [ Google Scholar ] [ CrossRef ]
• de Moura, M.F.S.F.; Gonçalves, J.P.M.; Silva, F.G.A. A New Energy Based Mixed-Mode Cohesive Zone Model. Int. J. Solids Struct. 2016 , 102–103 , 112–119. [ Google Scholar ] [ CrossRef ]
• Anderson, T.L. Fracture Mechanics: Fundamentals and Applications ; CRC Press: Boca Raton, FL, USA, 2016; pp. 1–630. [ Google Scholar ]
• Trawiński, W.; Bobiński, J.; Tejchman, J. Two-Dimensional Simulations of Concrete Fracture at Aggregate Level with Cohesive Elements Based on X-Ray ΜCT Images. Eng. Fract. Mech. 2016 , 168 , 204–226. [ Google Scholar ] [ CrossRef ]
• Wang, X.; Zhang, M.; Jivkov, A.P. Computational Technology for Analysis of 3D Meso-Structure Effects on Damage and Failure of Concrete. Int. J. Solids Struct. 2016 , 80 , 310–333. [ Google Scholar ] [ CrossRef ]
• Rodrigues, E.A.; Manzoli, O.L.; Bitencourt, L.A.G.; Bittencourt, T.N.; Sánchez, M. An Adaptive Concurrent Multiscale Model for Concrete Based on Coupling Finite Elements. Comput. Methods Appl. Mech. Eng. 2018 , 328 , 26–46. [ Google Scholar ] [ CrossRef ]
• Wang, Z.; Li, H.; Zhang, X.; Chang, Y.; Wang, Y.; Wu, L.; Fan, H. The Effects of Steel Fiber Types and Volume Fraction on the Physical and Mechanical Properties of Concrete. Coatings 2023 , 13 , 978. [ Google Scholar ] [ CrossRef ]
• EN 14889-2:2006 ; Fibres for Concrete-Part 2: Polymer Fibres-Definitions, Specifications and Conformity. European Committee for Standardization (CEN): Brussels, Belgium, 2006.
• ACI Comite 544. 3R. Guide for Specifying, Proportioning, and Production of Fiber-Reinforced Concrete ; American Concrere Institute: Farmington Hills, MI, USA, 2008; pp. 1–16. [ Google Scholar ]

Click here to enlarge figure

Share and Cite

Permanoon, A.; Pouraminian, M.; Khorami, N.; GanjiMorad, S.; Azarkhosh, H.; Sadrinejad, I.; Pourbakhshian, S. Improving Mixed-Mode Fracture Properties of Concrete Reinforced with Macrosynthetic Plastic Fibers: An Experimental and Numerical Investigation. Buildings 2024 , 14 , 2543. https://doi.org/10.3390/buildings14082543

Permanoon A, Pouraminian M, Khorami N, GanjiMorad S, Azarkhosh H, Sadrinejad I, Pourbakhshian S. Improving Mixed-Mode Fracture Properties of Concrete Reinforced with Macrosynthetic Plastic Fibers: An Experimental and Numerical Investigation. Buildings . 2024; 14(8):2543. https://doi.org/10.3390/buildings14082543

Permanoon, Ali, Majid Pouraminian, Nima Khorami, Sina GanjiMorad, Hojatallah Azarkhosh, Iman Sadrinejad, and Somayyeh Pourbakhshian. 2024. "Improving Mixed-Mode Fracture Properties of Concrete Reinforced with Macrosynthetic Plastic Fibers: An Experimental and Numerical Investigation" Buildings 14, no. 8: 2543. https://doi.org/10.3390/buildings14082543

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals