Predicting the crack repair rate of self-healing concrete using soft-computing tools

Abstract

To tackle the challenges of cracking and insufficient durability in traditional concrete, researchers are exploring self-healing concrete (SHC) as a potential alternative material. Nevertheless, doing laboratory studies can incur significant expenses and consume considerable time. Therefore, utilizing machine learning (ML) algorithms can contribute to advancing improved predictions for self-healing concrete. This study aimed to create ML models, specifically adaptive boosting (AB), artificial neural network (ANN), and gradient boosting (GB), to forecast and evaluate the repair rate of the cracked region in SHC that incorporates fibers and bacteria in their compositions. To further evaluate the importance of inputs, RReliefF analysis was performed. The findings indicated that the AB algorithm outperformed GB and ANN algorithms by achieving a coefficient of determination (R2) value of 0.987, a mean absolute error (MAE) value of 0.001 mm, and a root mean square error (RMSE) value of 0.026 mm. The GB approach yielded an R2 value of 0.962, an MAE value of 0.035 mm, and an RMSE value of 0.044 mm. Similarly, the ANN approach yielded an R2 value of 0.943, an MAE value of 0.040 mm, and an RMSE value of 0.054 mm. These results demonstrate that all AB, GB, and ANN algorithms outperformed in terms of prediction accuracy and model fit. Hence, the application of these models can be employed to construct and verify advanced SHC compositions that rely on polymeric fibers and bacteria, aiming to achieve superior performance. However, comparing the outcomes of developed models revealed that the AB model accuracy is higher than the GB and ANN models for predicting the area repair rate of cracks.