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Chuanchuan HOU, Huan WANG, Wei GUAN, Jun CHEN. Road pavement performance prediction using a time series long short-term memory model[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Road pavement performance prediction using a time series long short-term memory model",
author="Chuanchuan HOU, Huan WANG, Wei GUAN, Jun CHEN",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2300643"
}
%0 Journal Article
%T Road pavement performance prediction using a time series long short-term memory model
%A Chuanchuan HOU
%A Huan WANG
%A Wei GUAN
%A Jun CHEN
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2300643
TY - JOUR
T1 - Road pavement performance prediction using a time series long short-term memory model
A1 - Chuanchuan HOU
A1 - Huan WANG
A1 - Wei GUAN
A1 - Jun CHEN
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2300643
Abstract: 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 performance indicator for the pavement quality index (PQI-F), which leverages the highway performance assessment standards method, entropy weight method, and fuzzy comprehensive evaluation method. This indicator can evaluate the overall performance condition of 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 the 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-F) is 93.8%.
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