A Data-Driven Approach for Fatigue Damage Prediction in Jointed Plain Concrete Pavement Subjected to Superloads †

The passage of superloads over the jointed plain concrete pavements (JPCPs) causes signification fatigue damage to the JPCPs. This mainly happens because of their non-standardized loading configurations and high gross vehicle and axle weights. Developing a high-accuracy prediction model for JPCP fatigue damage under superloads is strongly required to complement the mechanistic–empirical (ME) pavement design in aspects of its wide range of dimensions, including number, spacing, and loading of tires and axles. In this study, various data-driven models based on different theoretical approaches, including artificial neural network-based models, generalized additive models, and multiple linear regression models, were constructed using a well-established database derived from finite-element analysis results in order to predict the target response for JPCP fatigue damage when subjected to superloads. The prediction accuracies of these data-driven models were then evaluated to confirm their further applicability to the existing ME pavement design software.