# Predicting Putting Development Using Machine Learning Methods Based on RIOCHTrack Data
## 1 Introduction
However, in the mechanical empirical model, the performance of the asphalt mixture was not fully considered, and influencing factors such as porosity, gradation, and asphalt content were ignored. With the collection and storage of massive test data, big data artificial intelligence technology is being increasingly applied to road engineering. Machine learning methods and big data have been applied to pavement performance prediction. The most widely used among them is ANN. [PERSON] [11] collected more than 5000 long-term performance data points from the United States and Canada, established an ANN model to predict pavement rutting, and adopted the random forest (RF) algorithm to optimize input variables. [PERSON]. [12] developed ANN to improve the prediction ability of rutting. [PERSON] [13] used RF to analyze the influence of a hot mixed asphalt mixture on rutting and adopted an ANN model [14] to analyze the rutting depth of the asphalt pavement surface, base, and subbase. [PERSON] et al. [15] utilized traffic, climate conditions, pavement thickness, and pavement materials as inputs based on the LTPP database to establish a deep neural network model for predicting pavement rutting. In these studies, they only established one model and were unable to compare the predictive performance of several models. In addition, [PERSON] [16] performed long-term research on an asphalt pavement (LTPP) database to select 27 feature variables, including temperature, axle load, pavement structure, and pavement material properties, for rutting prediction and compared four machine learning models (support vector machine (SVM), artificial neural network (ANN), gradient boosting (GB), and multiple linear regression model). Ref. [17] [PERSON] established several ML methods (regression trees, ANN, SVM, GPR, and ANN) to predict rutting using data from LTPP. However, the database they rely on to build models is LTPP. However, in terms of road structure and materials, there are many differences between North America and China. The generalization ability of ML models depends strongly on the range of datasets. However, a small amount of the published literature has established and compared various machine learning models based on full-scale pavement in China. So it is necessary to establish a research model for common road pavement in China. Establishing a suitable model requires experimental data. Rutting data can be acquired through indoor rutting tests, full-scale accelerated pavement tests, and operational monitoring of real road pavements [18]. The indoor rutting test is a test method formed by simulating actual wheel loads on road surfaces in the laboratory. Commonly used indoor rutting tests include the Hamburg rutting test, French rutting test, APA test, stability test, and flow value test [19]. [PERSON] et al. [20] investigated the influence of aging and rejuvenation conditions on rutting performance by conducting linear viscoelastic tests. [PERSON] et al. [21] used rutting failure temperature to assess the rutting potential and probed the rutting performance of two types of elastomer/plastic compound-modified bitumen. However, indoor test conditions differ from actual asphalt mixture conditions. The indoor rutting test is easy to operate, and the data is intuitive, but the climate and load conditions of the indoor test are greatly limited, making it difficult to accurately simulate the development of real rutting. Therefore, more and more researchers are committed to collecting operational data on real roads and establishing huge databases, with the most famous being the Long Term Pavement Performance (LTPP) database in the United States [22]. Although LTPP provides massive data support for long-term pavement performance research, the data quality is not satisfactory; moreover, it consumes a considerable amount of time and financial resources. Accelerated loading is mainly achieved using a full-scale pavement test loop, which can simultaneously compare the performance of various pavement structures and materials.