Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing

In Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no...

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Main Authors: Li Rui, Xie Xiaoyu, Duan Xueyan
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Mobile Information Systems
Online Access:http://dx.doi.org/10.1155/2021/9955988
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spelling doaj-2cb5742ee2194a2e874e086111f981fe2021-07-02T21:12:40ZengHindawi LimitedMobile Information Systems1875-905X2021-01-01202110.1155/2021/9955988Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless SensingLi Rui0Xie Xiaoyu1Duan Xueyan2The Faculty of Civil Engineering and MechanicsThe Faculty of Civil Engineering and MechanicsThe Faculty of Civil Engineering and MechanicsIn Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no reference vehicle load spectrum for fatigue analysis in the existing research. The application of wireless sensing technology to bridge health monitoring is favorable for the entire monitoring system’s low-cost and intelligent development. In this study, wireless sensors are used to collect sensing data in the measured area and perform preliminary filtering processing. The data collected by the sensing layer is aggregated at the TD gateway layer to realize local short-term storage of monitoring data, and 3G wireless transmission is used for the effective processing of the data. The clustering method is used to classify the vehicle models based on investigating the most representative expressway traffic flow information in Yunnan Province. Moreover, the weighted probability distribution model of different vehicle models is established through statistical analysis, which simplifies the composition’s fatigue intensity spectrum model. The selection of five vehicles of the equivalent model followed by a six-axle vehicle has the most significant impact on bridge damage as the standard fatigue vehicle. The research results establish a basis for the fatigue design of highway bridges in plateau and mountainous areas and provide data to establish vehicle fatigue load spectra in national highway regions.http://dx.doi.org/10.1155/2021/9955988
collection DOAJ
language English
format Article
sources DOAJ
author Li Rui
Xie Xiaoyu
Duan Xueyan
spellingShingle Li Rui
Xie Xiaoyu
Duan Xueyan
Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
Mobile Information Systems
author_facet Li Rui
Xie Xiaoyu
Duan Xueyan
author_sort Li Rui
title Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
title_short Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
title_full Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
title_fullStr Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
title_full_unstemmed Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing
title_sort fatigue load spectrum of highway bridge vehicles in plateau mountainous area based on wireless sensing
publisher Hindawi Limited
series Mobile Information Systems
issn 1875-905X
publishDate 2021-01-01
description In Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no reference vehicle load spectrum for fatigue analysis in the existing research. The application of wireless sensing technology to bridge health monitoring is favorable for the entire monitoring system’s low-cost and intelligent development. In this study, wireless sensors are used to collect sensing data in the measured area and perform preliminary filtering processing. The data collected by the sensing layer is aggregated at the TD gateway layer to realize local short-term storage of monitoring data, and 3G wireless transmission is used for the effective processing of the data. The clustering method is used to classify the vehicle models based on investigating the most representative expressway traffic flow information in Yunnan Province. Moreover, the weighted probability distribution model of different vehicle models is established through statistical analysis, which simplifies the composition’s fatigue intensity spectrum model. The selection of five vehicles of the equivalent model followed by a six-axle vehicle has the most significant impact on bridge damage as the standard fatigue vehicle. The research results establish a basis for the fatigue design of highway bridges in plateau and mountainous areas and provide data to establish vehicle fatigue load spectra in national highway regions.
url http://dx.doi.org/10.1155/2021/9955988
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