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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Advances in engineering software (1992) Jg. 173; S. 103267
Hauptverfasser: Rajakarunakaran, Surya Abisek, Lourdu, Arun Raja, Muthusamy, Suresh, Panchal, Hitesh, Jawad Alrubaie, Ali, Musa Jaber, Mustafa, Ali, Mohammed Hasan, Tlili, Iskander, Maseleno, Andino, Majdi, Ali, Ali, Shahul Hameed Masthan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2022
Schlagworte:
Compression strength
Decision tree
Machine learning
Random forest
Regression
Self-compacting concrete
Regression
Self-compacting concrete
Random forest
Decision tree
Compression strength
Machine learning
ISSN:0965-9978
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•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.
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2022.103267