Machine learning approach for investigating compressive strength of self-compacting concrete containing supplementary cementitious materials and recycled aggregate

Supplementary cementitious materials (SCMs) and recycled coarse aggregate (RCA) have the potential for sustainable development and resource utilization and have been widely applied in self-compacting concrete (SCC). However, traditional experiment approaches for predicting its compressive strength suffer from inefficiency, time-consuming, and high cost. To address these issues, machine learning (ML) models are employed, evaluated, and compared in this paper. First, a database of 337 samples with 10 features is collected and established. Then, the 12 ML models, including SVM, KNN, DT, RF, ANN, GBoost, XGBoost, AdaBoost, CatBoost, LightGBM, HistGBM, and GEP are applied for modeling to predict the compressive strength. Finally, both feature importance, individual conditional expectation, and partial dependence plot are utilized to assess the importance and the effect of each and coupled parameters. The results show that the CatBoost and LightGBM models generally possess superior performance in various aspects, including higher R2 values, lower RMSE, MSE, and MAE values, as well as smaller errors. Among the Boosting models, except for the AdaBoost model, satisfactory predictive ability is observed. The ANN and RF models also demonstrate relatively good performance, and the SVM, DT, KNN, and GEP models present relatively poorer performance. Curing age and W/CMs exhibit the most pivotal features on model prediction. Furthermore, RCA replacement ratio plays a more prominent role than CA, and SCMs contribute somewhat to the compressive strength of concrete, but not as much compared to cement. Also, it is important to acknowledge the significant contribution of SP. This study offers a systematic evaluation of the prediction of compressive strength in SCC containing SCMs and RCA, making a notable contribution to both the existing literature and practical applications.