# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 1 Introduction  
Transportation infrastructure projects have always been crucial assets for every country, playing a vital role in economic development and societal progress [1]. Pavements typically handle approximately 80% of passenger transportation tasks in various countries [2]. However, pavements can sustain damage to varying degrees during their service life due to factors such as traffic loads and environmental conditions. An annual expenditure of over USD 400 billion is allocated globally for pavement construction, maintenance, and repair (M&R) [3]. Rigid pavements are widely recognized for their durability, long lifespan, high resistance to heavy traffic, and adaptability to harsh weather conditions [4]. JPCP is the most common and extensively used type within rigid pavements [5]. However, with time, JPCP experiences various damages due to multiple influencing factors, such as faulting, pumping, and corner spalling. Among these factors, faulting is regarded as oneof the most significant structural and functional failures in JPCP, which is characterized by height differences along transverse joints. Faulting on the pavement surface result in increased road roughness, leading to higher fuel consumption, more frequent traffic accidents, increased greenhouse gas emissions, and higher maintenance costs, all of which are inconsistent with environmental requirements [6]. Extensive research has been conducted to develop accurate predictive models for faulting characteristics, highlighting its importance in the field. Historical prediction models for faulting have traditionally been categorized into two main types: regression-based models and machine-learning-based models. Initially, regression models were widespread. For instance, in 1994, [PERSON] et al. [7] created two distinct models to predict faulting in JPCP. One model focused on the presence of dowel bars, while the other aimed to address their absence. However, the R-squared values for these two models were 0.534 and 0.550, respectively, which suggests a low level of accuracy in real-world scenarios. The models' low accuracy can be attributed to the limited dataset size (less than one hundred) and the inability of a linear combination of input variables to capture the complex nonlinear relationships that influence pavement failures. Similarly, [PERSON] et al. [8] employed regression methods to develop a fault prediction model. To enhance the model's performance, the dataset was expanded to include 146 samples, and additional variables such as modified drainage coefficient and average transverse joint spacing were introduced. The model achieved an R-squared value of 0.60, indicating moderate improvement. The AASHTO report (2010) utilized a methodology of progressively increasing failure rates over time to represent the average faulting. Additional variables, including weather information, deformation, and erodibility factor, were incorporated into the model. Despite the improvement in the model's R-squared value to 0.71, it became excessively complex. [PERSON] et al. [9] employed various contemporary regression techniques, such as the generalized linear model (GLM) and generalized additive model (GAM), to enhance the accuracy of the model's predictions. Moreover, they identified variables with notable influences, such as pavement age, yearly ESALs, and so on, for the prediction of faulting. The model, developed utilizing 302 data points, achieved an R-squared value of 0.6039. Researchers have adopted machine learning techniques to address the challenges associated with predicting faulting due to the limitations of regression-based methods. [PERSON] et al. [10] attempted to develop a faulting prediction model using Artificial Neural Networks (ANNs). The model used input variables such as pavement age, base type and material description, base thickness, erodibility, resilient modulus, etc. The model attained an R\({}^{2}\) value of 0.94, indicating significantly higher accuracy compared to the linear-regression-based model with an R\({}^{2}\) value of 0.51.