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Improved model for pavement performance prediction based on recurrent neural network using LTPP database
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DOI：10.1016/j.ijtst.2024.08.005.png)
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摘要
En 中文
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 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 the long-term pavement performance (LTPP) database, the study screens and summarizes these indicators, obtaining 2 464 high-quality pavement performance data for pavement conditions index (PCI) prediction and 3 238 high-quality pavement performance data for international roughness index (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 the LSTM model, which makes the fitting accuracy of the prediction model further improved.
Keyword:
Pavement performance forecasting
Long-term pavement performance (LTPP) database
Recurrent neural network (RNN)
long short-term memory (LSTM)-attention
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被引数:
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引用论文
Recurrent Neural Networks for Pavement Performance Forecasting: Review and Model Performance Comparison用于路面性能预测的递归神经网络: 回顾和模型性能比较
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分享收藏Jiang, J., Ni, F., Dong, Q., Yao, L., 2019. Improving the quality of pavement performance data in pavement management system, in: CICTP 2019: Transportation in China - Connecting the World - Proceedings of the 19th COTA International Conference of Transportation Professionals. https://doi.org/10.1061/9780784482292.369江, J., 倪, F., 董Q., 姚L., 2019. 提高路面管理系统中的路面性能数据质量，载于：CICTP 2019: 中国交通 - 连接世界 - 第19届COTA交通专业国际会议论文集. https://doi.org/10.1061/9780784482292.369.png)
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