Prediction and Evaluation of Road Service Performance in Cold Regions Based on Uncertainty Quantification Theory

LIU Yuanyuan, School of Transportation Science and Engineering , Harbin Institute of Technology , Harbin ,Heilongjiang 150090 , ChinaFollow
SI Junling, School of Transportation Science and Engineering , Harbin Institute of Technology , Harbin ,Heilongjiang 150090 , China
ZHENG Hao, School of Transportation Science and Engineering , Harbin Institute of Technology , Harbin ,Heilongjiang 150090 , ChinaFollow
REN Daoju, School of Transportation Science and Engineering , Harbin Institute of Technology , Harbin ,Heilongjiang 150090 , China
FAN Shaobing, School of Transportation Science and Engineering , Harbin Institute of Technology , Harbin ,Heilongjiang 150090 , China
QUAN Lei, Research Institute of Highway Ministry of Transport , Beijing 100088 , China

Abstract

In cold regions,road service performance is influenced by various nonlinear dynamic factors under complex hydrothermal environments and freeze-thaw cycles.Traditional empirical and mechanical models exhibit significant limitations in comprehensively considering the impacts of complex environments and conducting quantitative analyses.This study proposed a method to predict and assess the service performance of constructed roads in cold regions.The method integrated deep neural networks with uncertainty quantification theory,addressing the inadequacies of existing models in adapting to complex environments and analyzing multiple influencing factors.By utilizing data from Montana road sections in the LTPP database,the study employed deep neural networks to predict the international roughness index (IIRI).The Morris one-at-a-time method was used to screen key influencing factors,and sensitivity analysis was conducted using the Sobol index to identify the primary factors affecting road service performance.The results indicate that road age,traffic volume,and freezing index are critical factors influencing road service performance,followed by temperature and wind speed,while annual precipitation,solar radiation,and humidity have relatively minor impacts.These findings highlight the significance of screening key factors and conducting sensitivity analyses to discern the primary and secondary factors influencing road service performance in the complex environments of cold regions.The proposed research framework effectively addresses the limitations of traditional evaluation models in adapting to complex environments and analyzing influencing factors comprehensively.It provides theoretical support and practical guidance for accurate prediction and scientific evaluation of road service performance in cold regions.

Publication Date

2-22-2025

DOI

10.14048/j.issn.1671-2579.2025.01.007

First Page

Last Page

Submission Date

March 2025

Recommended Citation

Yuanyuan, LIU; Junling, SI; Hao, ZHENG; Daoju, REN; Shaobing, FAN; and Lei, QUAN (2025) "Prediction and Evaluation of Road Service Performance in Cold Regions Based on Uncertainty Quantification Theory," Journal of China & Foreign Highway: Vol. 45: Iss. 1, Article 7.
DOI: 10.14048/j.issn.1671-2579.2025.01.007
Available at: https://zwgl1980.csust.edu.cn/journal/vol45/iss1/7

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