Simultaneous prediction the strain and energy absorption capacity of ultra-high performance fiber reinforced concretes by using multi-output regression model

Abstract

Although a few machine learning (ML) models have successfully been developed for Ultra-high performance fiber reinforced concrete (UHPFRC), they only limited to one output per model and do not explain the effects of its ingredients. This paper proposes an novel approach to tackle the multiple outputs problems by using multi-output regression model (MORM) and providing some insights into the relations between dosages and their outputs via partial dependence plots. A UHPFRC database with 980 mixtures designs with 34 features and two outputs, namely the strain at peak tensile stress and energy absorption capacity is used to verify the propose approach. The MORM with three estimators, namely XGBoost Regressor, Decision Tree Regressor and Gradient Boosting Regressor is trained and tested. Three quantitative measures (R2, MAE and RMSE) are employed to evaluate the accuracy and the obtained results are superior to previous study. Among the results found, the greater feasibility of deformed high-strength steel macrofibers to achieve their exceptional values can be highlighted. On the one hand, the energy absorption capacity directly depends on the cementitious matrix’s quality. On the other hand, the strain at peak tensile stress decreases with the improvement of the matrix’s compressive strength to around 200 MPa. From this resistance, the trend is reversed due to an exponential improvement caused by the improvement in the bond strength between fiber and matrix. The proposed method could be properly used for the optimization of new UHPFRC dosages.