Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques - 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 (Advances in engineering software (1992), 2022)
•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.