Prediction on tensile mechanical properties of engineered cementitious composites using a fully automated deep learning method

Accurately predicting tensile mechanical properties of Engineered Cementitious Composites (ECC), including tensile strength (TS) and ultimate tensile strain (UTS), remains challenging, particularly for the ultimate tensile strain, which exhibits high data dispersion that hinders reliable predictions. While machine learning (ML) has shown significant promise in predicting the mechanical properties of ECC, it faces several challenges: (1) limited predictive accuracy; (2) complexity and time-consuming nature of hyperparameter tuning; (3) insufficient research into predicting the UTS of ECC. To overcome these challenges, this study proposes a fully automated deep learning model enhanced by Bayesian optimization for predicting the tensile mechanical properties of polyethylene fiber-reinforced engineered cementitious composites (PE-ECC). First, convolutional neural network (CNN) model was employed to enhance feature extraction and predictive accuracy. Second, Bayesian optimization was utilized to automatically construct the network and optimize various hyperparameters, resulting in an optimized network. This research demonstrates the effectiveness of the proposed Bayesian-optimized convolutional neural network (BO-CNN) method in predicting PE-ECC's TS and UTS, utilizing a dataset of 154 experimental samples from published literature, achieving coefficients of determination () of 0.911 and 0.775, respectively. The BO-CNN method not only outperforms CNNs and artificial neural networks (ANNs) optimized through grid search but also surpasses other eleven traditional ML models. Furthermore, SHapley Additive exPlanations (SHAP) was employed to interpret input features' contributions in the prediction of tensile mechanical properties. Overall, the proposed method provides an efficient and reliable solution for predicting the tensile mechanical properties of ECC.