Prediction on compressive strength of recycled aggregate self-compacting concrete by machine learning method

Abstract

The compressive strength is generally a crucial mechanical indicator for evaluating the quality of recycled aggregate self-compacting concrete (RASCC). To obtain a reliable prediction result of the compressive strength of RASCC, machine learning methods, including artificial neural network (ANN), random tree (RT), bagging, and random forest (RF), are utilized to predict the compressive strength of RASCC in this study. To build predictive models, 18 features and 289 data samples were collected from previous literature. The prediction effects of 4 artificial intelligence (AI) models on compressive strength of RASCC are compared. Four statistical parameters were used to evaluate the performance of the models, and a comparison was made between the model-predicted results and the experimental results. A good correlation between machine learning models and experimental results was obtained. Sensitivity investigation reveals that the cement content and the apparent density of natural coarse aggregate have the greatest influence on compressive strength. The study demonstrates the potential of ANN and RF models as useful tools that can support mortar design and/or optimization, with higher accuracy and good interpretability. According to this study, machine learning regression techniques hold great potential as instruments for forecasting RASCC's compressive strength.