Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. (November 2022) - 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
Highlights: 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 R 2 . The Random forest algorithm also performs better than the other models found from the analysis. Abstract: 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 R 2 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.