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<p>Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement. To this end, we develop a time series long short-term memory (LSTM) model to predict key performance indicators (PIs) of pavement, namely the international roughness index (IRI) and rutting depth (RD). Subsequently, we propose a comprehensive performance indicator for the pavement quality index (PQI), which leverages the highway performance assessment standard method, entropy weight method, and fuzzy comprehensive evaluation method. This indicator can evaluate the overall performance condition of the pavement. The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks, called MnRoad and RIOHTrack. Six variables are used as predictors, including temperature, precipitation, total traffic volume, asphalt surface layer thickness, pavement age, and maintenance condition. Furthermore, wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy. In comparison to a traditional autoregressive integrated moving average (ARIMAX) model, the proposed LSTM model performs better in terms of PI prediction and resiliency to noise. Finally, the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.</p>

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Road pavement performance prediction using a time series long short-term memory (LSTM) model

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Chuanchuan Hou,
  • Huan Wang,
  • Wei Guan,
  • Jun Chen

摘要

Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement. To this end, we develop a time series long short-term memory (LSTM) model to predict key performance indicators (PIs) of pavement, namely the international roughness index (IRI) and rutting depth (RD). Subsequently, we propose a comprehensive performance indicator for the pavement quality index (PQI), which leverages the highway performance assessment standard method, entropy weight method, and fuzzy comprehensive evaluation method. This indicator can evaluate the overall performance condition of the pavement. The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks, called MnRoad and RIOHTrack. Six variables are used as predictors, including temperature, precipitation, total traffic volume, asphalt surface layer thickness, pavement age, and maintenance condition. Furthermore, wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy. In comparison to a traditional autoregressive integrated moving average (ARIMAX) model, the proposed LSTM model performs better in terms of PI prediction and resiliency to noise. Finally, the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.