Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniques

Engineered cementitious composite (ECC) is a special material that, when continuously hydrated, can considerably aid in self-healing. It is necessary to look at the capacity of ECC for self-healing – as measured by crack-width_after (CWA) – when process of healing has completed, gauge the severity of the cracking, and foresee the extent of the healing. However, modeling and forecasting capacity of ECC for self-healing is a challenging task. Prediction of self-healing is a notably uncommon application of machine learning (ML), which has been applied to forecast a range of concrete properties. To estimate the capacity of ECC for self-healing, this study used three different the ensemble ML algorithms namely AdaBoost regressor (AR), decision tree (DT), and bagging regressor (BR). In addition, k-fold cross-validation method is utilized to assess the model effectiveness. With an R² value of 0.974, the BR model was more successful in predicting outcomes when compared to the DT and AR models. Improved model performance was shown for ensemble models with smaller MAE (AR = 3.40, and BR = 1.89), MSE (AR = 27.09, and BR = 10.40), and RMSE (AR = 5.21, and BR = 3.23) values and larger R² (AR = 0.933, and BR = 0.974) values as compared to DT (MAE = 4.29, MSE = 43.28, RMSE = 6.58, R² = 0.894). Eventually this study will lead to savings in time, effort, and money, and the use of ML approaches to predict CWA will advance the field of civil engineering. It is also advised to investigate the crack-healing properties of additional cementitious materials such rice husk ash, wheat straw ash, and pumice powder subject to modeling their crack-healing properties using ML approaches.