# Predicting Putting Development of Pavement with Flexible Overlay Using Artificial Neural Network
## 1 Introduction  
In China, there exist numerous pavements with long service lives and serious cumulative damage, which need to be repaired urgently [1]. At present, using a flexible overlay to repair pavement has been widely adopted in the repair of old roads. It is necessary to investigate the performance of flexible overlay for repairing pavements. Putting is one of the most typical diseases of flexible pavement, and it affects driving safety [2]. Moreover, rutting is a significant indicator in the MEPDG to judge whether flexible pavements are going to fail. Thus, it is significant to the service performance management of pavements with flexible overlays [3]. A lot of research has been conducted to predict rutting development [4]. The simplest and most straightforward method is the empirical method, which predicts the rut depth with the pavement structure, material parameters, climate, and load using a statistical method [5]. [PERSON] [6] obtained regression formulas for predicting the rutting depth under different traffic loads and temperatures. Considering the different factors that affect rutting, such as temperature, shear stress of the pavement structure, surface thickness, vehicle speed, loading repetitions, and dynamic stability, a multivariate regression model for rutting was established in [7]. Without considering the overall response of the pavement structure and the mechanical parameters of the materials in the model established through empirical methods, the prediction of the rutting performance is very poor. In addition, the empirical method can only be applied to specific environments. The mechanistic-empirical (M-E) method establishes the empirical relationship between the pavement rutting and material characteristics, pavement structure, and number of loads. According to the deformation characteristics of the mixture, the mechanical response of the pavement is calculated following the elastic layered system theory, or viscoelastic theory [8]. This method has been widely applied in road surface research. The most representative prediction model is the AASHTO2002 model. Based on the theory of elastic layered systems, an MEPDG mechanical-empirical design method was proposed to calculate the permanent deformation of different structural layers. The prediction model in the MEPDG needs to be calibrated using national and local data, and many researchers have calibrated models for different states. However, with the popularization of the MEPDG in North America, more and more researchers have found that its accuracy in predicting the asphalt pavement performance, especially rutting, is not high. In addition, many researchers have proposed their own calibration models. [PERSON] [9] established a mechanical-empirical model to predict the development of rutting in different pavement structures based on data from a full-scale pavement track, and the impact of base types on rutting was analyzed. However, the influence of the asphalt mixture performance was not considered in the mechanical-empirical model. Both the empirical model and M-E model are deterministic models, but probabilistic models can also be used for rut prediction. The Markov prediction model is the most typical probability prediction model, and it is relatively complete in predicting the asphalt pavement performance, with significant characteristics and no aftereffects [10]. [PERSON] [11] and [PERSON] [12] used Bayesian regression and Markov chains to estimate the rutting depth under different confidence levels. With the collection and storage of a large quantity of test data, machine learning has been used in civil engineering industries [13; 14; 15]. Artificial neural networks (ANNs) are the most widely used. [PERSON] and [PERSON] [16] introduced an ANN to forecast the rut depth with the input of traffic and climate parameters and the pavement thickness. [PERSON] et al.