Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques
•The main objective of this work is to develop regression models for forecasting self-compacting concrete compressive strength that are based on machine learning.•The correctness of the model can be evaluated based on the RMSE value, as well as MSE, MAE, and R2.•The Random forest algorithm also perf...
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| Veröffentlicht in: | Advances in engineering software (1992) Jg. 173; S. 103267 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.11.2022
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| ISSN: | 0965-9978 |
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| Abstract | •The main objective of this work is to develop regression models for forecasting self-compacting concrete compressive strength that are based on machine learning.•The correctness of the model can be evaluated based on the RMSE value, as well as MSE, MAE, and R2.•The Random forest algorithm also performs better than the other models found from the analysis.
Self-Compacting Concrete (SCC) has congested structural components and an inaccessible position. Mixing concrete multiple times becomes time-consuming and expensive. Due to a lack of competence in mixture design, analyzing appropriate mixture components and their influence on SCC's mechanical behavior might be a real-time concern in the construction sector. The work intends to create machine learning-based regression models to predict SCC compressive strength. A laboratory set of data comprising 99 SCC samples was used for this purpose. SCC's machine-learning regression model has many input and output parameters. Python machine learning was used to compare actual strengths. Linear regression, Lasso regression, Ridge regression, multi-layer perceptron regression, decision tree regression, and random forest regression are machine learning prediction methods. RMSE, MSE, MAE, and R2 measure model accuracy. The Random Forest model can efficiently estimate self-compressing concrete compression strength, according to the results. The RF model forecasts concrete's compressive strength accurately. |
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| AbstractList | •The main objective of this work is to develop regression models for forecasting self-compacting concrete compressive strength that are based on machine learning.•The correctness of the model can be evaluated based on the RMSE value, as well as MSE, MAE, and R2.•The Random forest algorithm also performs better than the other models found from the analysis.
Self-Compacting Concrete (SCC) has congested structural components and an inaccessible position. Mixing concrete multiple times becomes time-consuming and expensive. Due to a lack of competence in mixture design, analyzing appropriate mixture components and their influence on SCC's mechanical behavior might be a real-time concern in the construction sector. The work intends to create machine learning-based regression models to predict SCC compressive strength. A laboratory set of data comprising 99 SCC samples was used for this purpose. SCC's machine-learning regression model has many input and output parameters. Python machine learning was used to compare actual strengths. Linear regression, Lasso regression, Ridge regression, multi-layer perceptron regression, decision tree regression, and random forest regression are machine learning prediction methods. RMSE, MSE, MAE, and R2 measure model accuracy. The Random Forest model can efficiently estimate self-compressing concrete compression strength, according to the results. The RF model forecasts concrete's compressive strength accurately. |
| ArticleNumber | 103267 |
| Author | Muthusamy, Suresh Panchal, Hitesh Jawad Alrubaie, Ali Rajakarunakaran, Surya Abisek Lourdu, Arun Raja Majdi, Ali Maseleno, Andino Musa Jaber, Mustafa Tlili, Iskander Ali, Shahul Hameed Masthan Ali, Mohammed Hasan |
| Author_xml | – sequence: 1 givenname: Surya Abisek surname: Rajakarunakaran fullname: Rajakarunakaran, Surya Abisek organization: Department of Civil Engineering, PSR Engineering College (Autonomous), Sivakasi, Tamil Nadu, India – sequence: 2 givenname: Arun Raja surname: Lourdu fullname: Lourdu, Arun Raja organization: Department of Civil Engineering, PSR Engineering College (Autonomous), Sivakasi, Tamil Nadu, India – sequence: 3 givenname: Suresh surname: Muthusamy fullname: Muthusamy, Suresh email: infostosuresh@gmail.com organization: Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India – sequence: 4 givenname: Hitesh surname: Panchal fullname: Panchal, Hitesh organization: Department of Mechanical Engineering, Government Engineering College Patan, Gujarat, India – sequence: 5 givenname: Ali surname: Jawad Alrubaie fullname: Jawad Alrubaie, Ali organization: Department of Medical Instrumentation Techniques Engineering, Al- Mustaqbal University College, Hilla 51001, Iraq – sequence: 6 givenname: Mustafa surname: Musa Jaber fullname: Musa Jaber, Mustafa organization: Department of Medical Instruments Engineering Techniques, Dijlah University College, Al-Farahidi University, Baghdad 10021, Iraq – sequence: 7 givenname: Mohammed Hasan surname: Ali fullname: Ali, Mohammed Hasan organization: Computer technique engineering department, Faculty of Information Technology, Imam Jafar Al-sadiq University, Iraq – sequence: 8 givenname: Iskander surname: Tlili fullname: Tlili, Iskander organization: Physics Department, College of Science, Al-Zulfi, Majmaah University, AL-Majmaah 11952, Saudi Arabia – sequence: 9 givenname: Andino surname: Maseleno fullname: Maseleno, Andino organization: Department of Information Systems, STMIK Pringsewu, Lampung, Indonesia – sequence: 10 givenname: Ali surname: Majdi fullname: Majdi, Ali organization: Department of Building and Construction Techniques Engineering, Al- Mustaqbal University College, Hilla 51001, Iraq – sequence: 11 givenname: Shahul Hameed Masthan surname: Ali fullname: Ali, Shahul Hameed Masthan organization: Department of Civil Engineering, PSR Engineering College (Autonomous), Sivakasi, Tamil Nadu, India |
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| Keywords | Regression Self-compacting concrete Random forest Decision tree Compression strength Machine learning |
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| SubjectTerms | Compression strength Decision tree Machine learning Random forest Regression Self-compacting concrete |
| Title | Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques |
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