Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a m...

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Main Authors: Qingqing Zhang, Wenju Zhao, Jian Zhang
Format: Article
Language:English
Published: RTU Press 2021-09-01
Series:The Baltic Journal of Road and Bridge Engineering
Subjects:
Online Access:https://bjrbe-journals.rtu.lv/article/view/5121
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spelling doaj-363db401214248d9b2ced5b91d4bfeb12021-10-07T05:52:23ZengRTU PressThe Baltic Journal of Road and Bridge Engineering1822-427X1822-42882021-09-0116313115810.7250/bjrbe.2021-16.5352668Moving Load Identification with Long Gauge Fiber Optic Strain SensingQingqing Zhang0Wenju Zhao1Jian Zhang2School of Civil Engineering, Sichuan Agricultural University, Dujiangyan 611830, ChinaJiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing 210096, ChinaJiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing 210096, ChinaMoving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.https://bjrbe-journals.rtu.lv/article/view/5121axle parametersinfluence line theorylong gauge strainmaximum strainmoving load identification
collection DOAJ
language English
format Article
sources DOAJ
author Qingqing Zhang
Wenju Zhao
Jian Zhang
spellingShingle Qingqing Zhang
Wenju Zhao
Jian Zhang
Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
The Baltic Journal of Road and Bridge Engineering
axle parameters
influence line theory
long gauge strain
maximum strain
moving load identification
author_facet Qingqing Zhang
Wenju Zhao
Jian Zhang
author_sort Qingqing Zhang
title Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
title_short Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
title_full Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
title_fullStr Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
title_full_unstemmed Moving Load Identification with Long Gauge Fiber Optic Strain Sensing
title_sort moving load identification with long gauge fiber optic strain sensing
publisher RTU Press
series The Baltic Journal of Road and Bridge Engineering
issn 1822-427X
1822-4288
publishDate 2021-09-01
description Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.
topic axle parameters
influence line theory
long gauge strain
maximum strain
moving load identification
url https://bjrbe-journals.rtu.lv/article/view/5121
work_keys_str_mv AT qingqingzhang movingloadidentificationwithlonggaugefiberopticstrainsensing
AT wenjuzhao movingloadidentificationwithlonggaugefiberopticstrainsensing
AT jianzhang movingloadidentificationwithlonggaugefiberopticstrainsensing
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