Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle

Xu, Zheng; Yang, Dong‐Hui; Yi, Ting‐Hua; Li, Hong‐Nan

doi:10.1002/stc.2544

Cited by 48 publications

(9 citation statements)

References 34 publications

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“…While ambient vibrations [1][2][3] can be utilized to detect the modal properties of a bridge, vehicle-generated loads appear to be a more attractive means. [4][5][6][7][8][9] Some reviews on the vibration-based SHM using the vibration data directly recorded for the bridge are available in Carden and Fanning 10 and Fan and Qiao. 11 Recently, an instrumented test vehicle was proposed by Yang et al 12 to scan the modal properties of bridges.…”

Section: Introductionmentioning

confidence: 99%

“…Conventionally, structure health monitoring (SHM) has been carried out using the vibration data recorded by sensors directly deployed on the bridge. While ambient vibrations 1–3 can be utilized to detect the modal properties of a bridge, vehicle‐generated loads appear to be a more attractive means 4–9 . Some reviews on the vibration‐based SHM using the vibration data directly recorded for the bridge are available in Carden and Fanning 10 and Fan and Qiao 11…”

Section: Introductionmentioning

confidence: 99%

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Using vehicle–bridge contact spectra and residue to scan bridge's modal properties with vehicle frequencies and road roughness eliminated

Yang

Wang

et al. 2022

Structural Contr & Hlth

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using vehicle–bridge contact spectra and residue to scan bridge's modal properties with vehicle frequencies and road roughness eliminated

Yang

Wang

et al. 2022

Structural Contr & Hlth

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“…OBrien et al 19 first established a time domain mathematical model for bridge influence line identification and applied the least square method to determine bridge influence lines. Zheng et al 20 used the empirical mode decomposition method to extract bridge influence line from dynamic response induced by high speed vehicle. Chen et al 16,21 then introduced the regularization penalty function to the least square method to reduce the unreasonable fluctuation in the identified result.…”

Section: Introductionmentioning

confidence: 99%

Bridge influence surface identification method considering the spatial effect of vehicle load

Yang

et al. 2021

Struct Control Health Monit

Self Cite

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Summary Bridge influence surface is an important tool to study the bridge responses under the moving loads, which contains tremendous structural information. The structural deterioration during the service life would induce the variation of the bridge influence surface, which makes it essential to identify the influence surface exactly from the field measured data under moving vehicle loads. This paper firstly summarizes current development of bridge influence surface identification methods and classifies the methods in which the vehicle is respectively equivalent to line loads and concentrated loads. After that, the actual spatial distribution of the vehicle load is considered, based on which a new identification method is presented to solve the boundary problem of current influence surface identification methods. Then a numerical slab model with two fixed ends is illustrated to prove the validity of the method, and the accuracy and identifiable region of different methods in this condition are compared with the other methods. Influence factors of identification accuracy including noise level, axle number of vehicle and loading point are analyzed. Finally, laboratory verification using the same slab model is prepared to verify the effectiveness of the proposed identification method. Results of this study provide an effective identification method of bridge influence surface with very high identification accuracy, which can be used to full bridge influence surface test.

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“…In vibration analysis, an appropriate method separates the fluctuation from the measured responses to estimate bridge IL with quasi-static features (Strauss et al, 2012). The dynamic fluctuation part of structural dynamic responses induced by a high-speed vehicle is eliminated based on empirical mode decomposition to obtain bridge IL (Zheng et al, 2020). Considering the effect of the random vibration of the bridge, the dynamic IL is solved by using the virtual excitation method and the vehicle-bridge coupling equation (Xu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Eliminating the Influence of Axle Parameters in Influence Line Identification

Zhang

Liu

et al. 2021

BJRBE

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Accurate and rapid acquisition of the strain influence line of continuous beam plays a positive role in promoting the wide application of structural health monitoring. The structural response obtained from the sensors is used to estimate the strain influence line. However, most estimation methods ignore the influence of axle parameters on the structural response, resulting in a large error in identifying the strain influence line. This paper presents a method for eliminating the influence of axle parameters of moving vehicles on strain responses to estimate the strain influence line of continuous beams based on the long-gauge strain sensing technology. By analysing the mechanical characteristics of the multi-span continuous beam, a theoretical strain influence line expression is first established to obtain the strain influence line of the continuous beam accurately. The structural response only caused by axle weight, obtained by eliminating the influence of axle parameters, is then estimated for calibrating the theoretical strain influence line. Finally, different lane tests are also considered to solve the influence of different transverse position relations on the proposed method between the monitoring unit and the lane. Finally, numerical simulations are adopted to illustrate the effectiveness of the proposed identification method by simulating the strain time histories induced by a multi-axle vehicle. A field test also demonstrates the validity and feasibility of this method.

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Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle

Cited by 48 publications

References 34 publications

Using vehicle–bridge contact spectra and residue to scan bridge's modal properties with vehicle frequencies and road roughness eliminated

Using vehicle–bridge contact spectra and residue to scan bridge's modal properties with vehicle frequencies and road roughness eliminated

Bridge influence surface identification method considering the spatial effect of vehicle load

Eliminating the Influence of Axle Parameters in Influence Line Identification

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