# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
###### Abstract  
Traditional methods for predicting pavement performance rely on complex finite element modelling and empirical equations, which are computationally expensive and time-consuming. However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. The input variables include axle load, truck load, traffic speed, lateral wander modes, asphalt layer thickness, traffic lane width and tire types, while the output variables consist of number of passes to fatigue damage, number of passes to rutting damage, fatigue life reduction in number of years and rut depth at 1.3 million passes. A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R2 values. In contrast, linear regression and KNN demonstrated the lowest performance, with MSE values up to 188% higher than CatBoost. This study concludes that integrating machine learning with finite element analysis provides further improvements in pavement performance predictions. machine learning; rutting; fatigue; finite element modelling 2020 ac  
of uniform wander mode can be further increased by adding the truck platooning function, which works in favour of increased fuel efficiency for autonomous trucks [3]. Machine learning is part of the artificial intelligence types used in performance prediction modelling of impacts on pavements. Traditional methods for performance evaluation of pavements require complicated calculations and procedures to analyse the data and results. With the provision of a vast variety of available data, predictions based on pavement material properties, traffic details and environmental details can be made. Therefore, the model structure is designed based on the quantitative relationship between input variables and output variables, thereby developing the machine learning model [4]. The concept of machine learning is used to analyse large data and performance predictions of future pavement performance parameters based on training the algorithm. Machine learning is trained through the examples generated through various iterations of data and developing the relationship between input and output variables [5]. The artificial neural network (ANN) is composed of several interconnected processing units present in layer forms (input layers, hidden layers, output layer). The ANN is composed of three different types, consisting of the recurrent neural network (RNN), convolutional neural network (CNN) and multi-layer perception neural network (MLPNN), with hidden layers greater than one being part of the deep neural network [6]. A variety of parameters related to autonomous trucks in terms of traffic speed, lateral wander options, tire types and axle configurations are taken into account. Furthermore, the aforementioned parameters are combined, and their effect on putting and fatigue damage is evaluated using the data obtained from finite element modelling. The use of machine learning models for fatigue cracking and rutting progression of pavement based on numerous input parameters can significantly reduce the calculation time and quantity of data needed for analysis, since these models are fundamentally proficient for their role in pattern recognition, classification, data identification and predictions [7]. For fatigue cracking predictions, transfer functions are employed for alligator cracking and longitudinal cracking [8]. The term Stress Intensity Factor (SIF) has been used previously for performing cracking analysis of asphalt pavements [9]. Semi-analytical finite element modelling in conjunction with neural networks is performed for evaluating SIF.