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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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 |
_version_ |
1716839638637740032 |