Use of machine learning models to predict the water penetration depth in concrete

Abstract

Machine learning (ML) is a robust tool within the artificial intelligence domain that offers unique solutions for predictive modeling. Prediction of water penetration depth (Wpen) is crucial for assessing the durability and service life of concrete while reducing reliance on complex and time-consuming laboratory tests. This study investigates the impact of concrete composition, age, and compressive strength on Wpen using a dataset of 311 concrete specimens. Multiple supervised ML models were employed in predicting Wpen, including linear regression (LR), Gaussian process regression (GPR), support vector machine (SVM), random forest (RF), regression tree (RT), and hybrid RF-SVM models. Results revealed that hybrid RF-SVM model and regression tree accurately predicted Wpen. The models’ performance improved by including concrete age and compressive strength. The models were validated using data from relevant literature. This research provides valuable insights into predicting water penetration depth in concrete, offers practical tools for assessing concrete durability, and offers a more sustainable approach than laboratory testing.