Gaussian process regression model for the prediction of the compressive strength of polyurethane-based polymer concrete for runway repair: A comparative approach

Abstract

Polyurethane (PU) composites have increasingly been used as construction materials to maintain civil engineering structures such as road pavement, runway, parking area, and floor systems in buildings. This study developed polyurethane polymer concrete (PC) mixtures by mixing aggregate-to-PU resin at 0.9: 0.1 and 0.85: 0.15 ratios by weight. The Machine Learning algorithms, including Gaussian Process Regression (GPR), Classification and Regression Tree (CART), and Support Vector Regression (SVR) model were employed to predict the compressive strength of PUPC mixtures as a repair material. The models were trained on the dataset of flexural strength (MPa), density (kg/m3), and PU composition (%), applied as input variables. The result revealed that the compressive stress-strain curves of PU-based polymer concrete exhibit linear elastic behavior under compression. The developed models demonstrate high prediction accuracy of PUPC' strength. The Nash-Sutcliffe efficiency (NSE) was used to check the performance of each model, and the result obtained showed that the GPR model predicted the compressive strength with the highest accuracy with an NSE-values of 0.9619 and 0.9585 at the training and testing phase, respectively. The finding in this study could offer valuable insight into using these proposed models for compressive strength prediction of PU-based polymer concrete