Machine learning for predicting long-term deflections in reinforce concrete flexural structures

Abstract

Prediction of deflections of reinforced concrete (RC) flexural structures is vital to evaluate the workability and safety of structures during its life cycle. Empirical methods are limited to predict a long-term deflection of RC structures because they are difficult to consider all influencing factors. This study presents data-driven machine learning (ML) models to early predict the long-term deflections in RC structures. An experimental dataset was used to build and evaluate single and ensemble ML models. The models were trained and tested using the stratified 10-fold cross-validation algorithm. Analytical results revealed that the ML model is effective in predicting the deflection of RC structures with good accuracy of 0.972 in correlation coefficient (R), 8.190 mm in root mean square error (RMSE), 4.597 mm in mean absolute error (MAE), and 16.749% in mean absolute percentage error (MAPE). In performance comparison against with empirical methods, the prediction accuracy of the ML model improved significantly up to 66.41% in the RMSE and up to 82.04% in the MAE. As a contribution, this study proposed the effective ML model to facilitate designers in early forecasting long-term deflections in RC structures and evaluating their long-term serviceability and safety.