On the use of Synthetic Data for Machine Learning prediction of Self-Healing Capacity of Concrete

Abstract

This work investigated the use of synthetic data to overcome the limitations of scarce experimental data in predicting the self-healing capacity of bacteria-driven concrete. We generated a synthetic dataset based on real-world data, significantly expanding the original dataset and then trained and compared machine learning models, including probabilistic and ensemble methods, to predict the concrete self-healing capacity. The results demonstrate that the ensemble methods, particularly the random forest (RF) method (accuracy = 0.863 and F1-score = 0.863), outperformed the probabilistic models and achieved high accuracy in predicting self-healing capacity. The trained models were further applied to real-word data examples, showing high accuracy. This research validates the utility of synthetic data in predicting modelling accuracy and reliability in civil engineering, particularly in areas with limited experimental data. The findings contribute to the growing use of ML and AI in concrete research and demonstrate the transformative potential of synthetic data in addressing challenges in civil engineering.