A B-WIM algorithm considering the modeling of the bridge dynamic response

Gonçalves, Matheus Silva; Carraro, Felipe; López, Rosalía Rodríguez

doi:10.1016/j.engstruct.2020.111533

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…Latest generation B-WIM systems use BIL calibration from measured bridge response. 128 Boundary conditions of a real bridge are somewhat between simply supported and fixed, so getting accurate BIL is difficult. 70 There are basically two categories of methods for extracting actual BIL.…”

Section: Instrumentation On a Bridgementioning

confidence: 99%

“…Different methods and detailed comparative reviews on BIL identification techniques can be found in literature. 55,[128][129][130][131][132][133][134][135][136][137][138][139][140][141][142] To have a brief insight on how BIL is extracted using bridge response time histories, basic fundamental method is briefly described for completeness of the review.…”

Section: Instrumentation On a Bridgementioning

confidence: 99%

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Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Paul

Roy

2023

Structural Health Monitoring

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Bridge health monitoring (BHM) has grown as an essential component of the maintenance paradigm in any transportation network around the world. The mostly used visual inspection method has limitations such as inspector bias, inaccessibility in remote areas, need for human physical presence at the bridge location, etc. On the other hand, vibration-based BHM methods require artificial excitation to the bridge. This motivates researchers to explore potential alternatives for BHM. The bridge weigh-in-motion (B-WIM) system has emerged as a promising alternative with the potential to augment traditional BHM techniques. The advantages of B-WIM systems are durability, portability, easy installation, etc. In addition to weight estimation of passing vehicles, it provides other structural informations that are helpful for bridge assessment. It also overcomes the limitations of pavement-based WIM systems. Most importantly, a B-WIM system uses the same components as a standard BHM system, making BHM a by-product and economical compared to a standalone BHM system. With the development of B-WIM theory, recent researches have revealed the feasibility of B-WIM system for BHM. However, the applicability of the B-WIM system for BHM in any type of bridge and condition is limited. Through the fundamentals of B-WIM theory, this paper reviews the existing bridge response-based BHM methods that involved B-WIM algorithm. The findings are discussed in order to identify the remaining challenges and provide insight to the future aspects.

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Section: Instrumentation On a Bridgementioning

confidence: 99%

Section: Instrumentation On a Bridgementioning

confidence: 99%

Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Paul

Roy

2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Put another way, the influence line theory is a static concept, so the raw strain data measured by strain sensors are not applicable to the influence line theory because real bridges are subject to various dynamic loads. 42 To render the influence line theory applicable, it is necessary to preprocess the raw strain signals so that the static strain can be extracted. According to Jian et al, 31 the raw bridge strain signals contain mainly two components, which are non-vehicle component and vehicle-induced component, and the latter one can be further divided into vehicle-induced dynamic component and vehicle-induced static component.…”

Section: Preprocessing Of Bridge Strain Signalsmentioning

confidence: 99%

“…As discussed earlier, the BWIM technique is built on the influence line theory, 41 which describes the relationship among static load, load position, and static bridge responses. Put another way, the influence line theory is a static concept, so the raw strain data measured by strain sensors are not applicable to the influence line theory because real bridges are subject to various dynamic loads 42 . To render the influence line theory applicable, it is necessary to preprocess the raw strain signals so that the static strain can be extracted.…”

Section: Strain Sensingmentioning

confidence: 99%

Integrating bridge influence surface and computer vision for bridge weigh‐in‐motion in complicated traffic scenarios

Jian

Xia

Sun³

et al. 2022

Structural Contr & Hlth

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Complicated traffic scenarios, including random change of vehicles' speed and lane, as well as the simultaneous presence of multiple vehicles on bridge, are main obstacles that prevents bridge weigh-in-motion (BWIM) technique from reliable and accurate application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method which integrates deep-learning-based computer vision technique and bridge influence surface theory. In this study, bridge strains and traffic videos are recorded synchronously as the data source of BWIM. The computer vision technique is employed to detect and track vehicles and corresponding axles from traffic videos so that spatio-temporal paths of vehicle loads on the bridge can be obtained. Then a novel method is proposed to identify the strain influence surface (SIS) of the bridge structure based on the time-synchronized strain signals and vehicle paths. After the SIS is identified, the axle weight (AW) and gross vehicle weight (GVW) can be identified by integrating the SIS, time-synchronized bridge strain, and vehicle paths. For illustration and verification, the proposed method is applied to identify AW and GVW in scale model experiments, in which the vehicle-bridge system is designed with high fidelity, and various complicated traffic scenarios are simulated. Results confirm that the proposed method contributes to improve the existing BWIM technique with respect to complicated traffic scenarios.

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“…As discussed earlier, the BWIM is essentially the inverse problem of the influence line theory. The influence line theory is built on the static structural analysis, whereas the bridge strain signals measured in practice are inherently dynamic due to the moving vehicle, 34 and the frequently observed drift of strain signals needs to be considered as well. Therefore, the measured strain signals are pre‐processed first in this study to remove the unwanted trend and dynamic components.…”

Section: Experimental Verificationmentioning

confidence: 99%

A robust bridge weigh‐in‐motion algorithm based on regularized total least squares with axle constraints

Jian

Lai

Xia

et al. 2022

Structural Contr & Hlth

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Summary The identification of traffic loads, including the axle weight (AW) and the gross vehicle weight (GVW) of vehicles, plays an important role in bridge design refinement, safety evaluation, and maintenance strategies. Bridge weigh in motion (BWIM) is a promising technique to weigh vehicles passing through bridges. Though the state‐of‐the‐art BWIM can accurately identify the GVW, unacceptable weighing errors are reported when identifying the AW of vehicles, particularly for those with closely spaced axles. To address the axle weighing problem, this paper aims to improve the performance of BWIM in weighing individual axle loads apart from the gross vehicle weight. The work first theoretically analyzes the possible sources of errors of existing BWIM algorithms, which are observational errors residing in the BWIM equation, no constraint imposed on individual axle loads, and ill‐conditioned nature. Accordingly, three measures are taken to establish a novel robust BWIM algorithm, which is based on regularized total least squares, as well as imposing constraints on the relationship between axles. To validate the proposed algorithm, a series of weighing experiments are carried out on a high‐fidelity vehicle‐bridge scale model. The corresponding results indicate that the proposed BWIM algorithm significantly outperforms other existing BWIM algorithms in terms of the accuracy and robustness of identifying individual axle weight, while retaining satisfactory identification of the gross vehicle weight as well.

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A B-WIM algorithm considering the modeling of the bridge dynamic response

Cited by 21 publications

References 23 publications

Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Integrating bridge influence surface and computer vision for bridge weigh‐in‐motion in complicated traffic scenarios

A robust bridge weigh‐in‐motion algorithm based on regularized total least squares with axle constraints

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