Improved model for pavement performance prediction based on recurrent neural network using LTPP database

Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments, enabling the achievement of better pavement performance with limited financial resources. However, due to the intricate influence of numerous factors on pavement performance deterioration, improving the accuracy of pavement performance prediction poses a challenge for conventional models. Therefore, the aim of this study is to establish a machine learning-based model pavement performance prediction model. First, this study considers five factors that affect pavement performance, including pavement initial performance indicators, traffic loads, weather, pavement structure, and maintenance measures, and identifies 15 specific indicators that affect pavement performance based on these five factors. Then, based on the Long-Term Pavement Performance (LTPP) database, the study screens and summarizes these indicators, obtaining 2464 high-quality pavement performance data for Pavement Conditions Index (PCI) prediction and 3238 high-quality pavement performance data for IRI prediction. Finally, three distinct prediction models are established, namely, the Fully Connected Neural Network (FCNN) model, the Long Short-Term Memory (LSTM) model, and the combined LSTM-Attention model. The study shows that the LSTM-Attention model performs significantly better than the FCNN and LSTM models, with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI. The innovation of this paper is that the authors have introduced the Attention mechanism on the basic of LSTM model, which makes the fitting accuracy of the prediction model further improved.