Impact Strength Properties and Failure Mode Classification of Concrete U-Shaped Specimen Retrofitted with Polyurethane Grout Using Machine Learning Algorithms

Abstract

The inherent brittle behavior of cementitious composite is considered one of its weaknesses in structural applications. This study evaluated the impact strength and failure modes of composite U-shaped normal concrete (NC) specimens strengthened with polyurethane grout material (NC-PUG) subjected to repeated drop-weight impact loads (USDWIT). The experimental dataset was used to train and test three machine learning (ML) algorithms, namely decision tree (DT), Naïve Ba yes (NB), and K-nearest neighbors (KNN), to predict the three failure modes exhibited by U-shaped specimens during testing. The uncertainty of the failure modes under different uncertainty degrees was analyzed using Monte Carlo simulation (MCS). The results indicate that the retrofitting effect of polyurethane grout significantly improved the impact strength of concrete. During testing, U-shaped specimens demonstrated three major failure patterns, which included mid-section crack (MC), crushing foot (CF), and bend section crack (BC). The prediction models predicted the three types of failure modes with an accuracy greater than 95%. Moreover, the KNN model predicted the failure modes with 3.1% higher accuracy than the DT and NB models, and the accuracy, precision, and recall of the KNN model have converged within 300 runs of Monte Carlo simulation under different uncertainties.