Pavement Roughness Prediction Based on Encoder-decoder Structure

被引：0

作者：

Guo R. ^{[1
]}

Yu X. ^{[1
]}

机构：

[1] School of Civil Engineering, Tsinghua University, Beijing

来源：

Tongji Daxue Xuebao/Journal of Tongji University | 2023年 / 51卷 / 08期

关键词：

attention mechanism; encoder-decoder structure; gated recurrent unit (GRU); long short-term memory (LSTM) network; pavement roughness prediction;

D O I：

10.11908/j.issn.0253-374x.23166

中图分类号：

学科分类号：

摘要：

A pavement roughness prediction model based on encoder-decoder structure was proposed， and a comparative analysis of different layers was conducted. Then，the effect of the layer number，the number of hidden units and the time window length on the accuracy of the model was discussed. To train and evaluate the model，an international roughness index （IRI） dataset was constructed based on long-term pavement performance （LTPP） database published by the US Department of Transportation. The results show that the encoder-decoder structure with gated recurrent unit （GRU）layer has the highest accuracy，its performance is better than that of the machine learning model XGBoost and single long short-term memory（LSTM）network. The importance of different input features was evaluated by randomly shuffling features，and the results indicate that the road structure and temperature are important for pavement roughness prediction. Therefore， the road structure and temperature data should be attached great importance during the construction of pavement database. © 2023 Science Press. All rights reserved.

引用

页码：1182 / 1190

页数：8

共 30 条

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