# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 1 Introduction
This suggests that neural network algorithms, which can handle complex nonlinear relationships, are better suited for fault prediction than linear regression algorithms. Moreover, [PERSON] and [PERSON] [11] conducted a detailed study in which they developed four ANN models utilizing the ANN approach. They categorized the input variables into four groups: 4, 6, 8, and 10 variables. The ANN model, which consisted of eight variables, was ultimately recognized as the superior choice due to its low computation error and high computational speed. Nevertheless, the overall accuracy of the developed models in this study was not high, primarily attributed to the unreasonable settings of ANN hyperparameters and inappropriate feature selection. Hence, [PERSON] et al. [12] commenced their study by addressing the challenge of accurate feature selection in ANN prediction models. Their research involved initially developing a model that incorporated all input variables and subsequently applying the NSGA II-MLP algorithm to identify the essential variables. Two simplified models were constructed using ANN and RF, respectively. Through comparison and analysis, they identified the top-performing model, which achieved an R\({}^{2}\) value of 0.92. In a similar vein, [PERSON] et al. [13] employed metaheuristic algorithms to select features and optimize ANN hyperparameters in another study. Following hyperparameter optimization, the best model attained an R\({}^{2}\) value of 0. 976. Although ANN has demonstrated excellent accuracy, it still lacks interpretability. This is due to the fact that ANN is commonly regarded as a \"black box\"model, meaning it lacks the ability to explicitly explain the relationship between input variables and output results. In real-world applications, pavement management personnel aim to identify key variables that have a significant impact on pavement performance. This strategic knowledge not only enhances the reliability of the models, but also helps reduce data collection expenses. Traditional methods in the field of feature selection primarily minimize the loss function by choosing a subset of features that are specific to the model. The Boruta algorithm serves a distinct purpose by identifying all feature subsets associated with the dependent variable [14]. Alongside, SHAP and TPE are powerful analysis tools. SHAP applies game theory to calculate the marginal contribution of each feature to the model's output, assigning specific importance values to ensure both global and local interpretability [15]. As a result, SHAP has been widely applied in various domains. On the other hand, hyperparameter optimization involves finding the best combination of hyperparameters for a machine learning model. Setting hyperparameters is a complicated process that can significantly affect model performance. A Python library, TPE, employs a Bayesian optimization algorithm and surrogate targets to promptly and accurately determine the optimal hyperparameters, thus enhancing the model's performance [16; 17; 18; 19]. Combining the Boruta algorithm improves both the speed and quality of feature selection. Additionally, the addition of the TPE learning framework allows for automatic optimization of Bayesian hyperparameters, alleviating issues such as overfitting and the low generalization ability of the base model. This results in a faster and more cost-efficient approach to model building, with improved predictive performance. Finally, the incorporation of the SHAP interpretability framework enables the explanation of the actual impact of input variables on the final model. Using this approach leads to a precise model interpretation, streamlining practical applications and analytical procedures. Existing fault prediction models often rely on empirical methods for variable selection, lacking scientific rigor. Additionally, the black box nature of these models limits the interpretability of the results. In recent years, rigid pavements have gained increasing attention due to their long lifespan and durability.