State-of-the-art review on bridge weigh-in-motion technology

Yu, Yang; Cai, C.S.; Deng, Lu

doi:10.1177/1369433216655922

Cited by 190 publications

(105 citation statements)

References 71 publications

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“…Bridge structures are amongst the most critical components of transport infrastructure. Similar to other structures, they deteriorate over their lifetimes due to aging, environmental conditions and traffic loading [1][2][3]. Structurally deficient bridges may be unsafe, and continuous monitoring of bridges is desirable in such situations.…”

Section: Introductionmentioning

confidence: 99%

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

et al. 2020

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This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

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Section: Introductionmentioning

confidence: 99%

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

et al. 2020

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“…To this end, a number of studies on traffic information identification have been conducted. Among these methods, the bridge-weighin-motion (BWIM) technique is highlighted [4,5].…”

Section: Introductionmentioning

confidence: 99%

Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

et al. 2019

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Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system.

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“…Therefore, monitoring traffic load, including vehicle weight, velocity, quantity, type and trajectory, is crucial for bridge design refinement and safety assessment, as well as operational management. In order to monitor the traffic load of bridges, the bridge weigh-in-motion (BWIM) technique is highlighted [10]. The initial concepts behind BWIM were proposed by Moses [11], who used an instrumented bridge as the weighing scale to estimate vehicle weights in his engineering practice.…”

Section: Introductionmentioning

confidence: 99%

Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

et al. 2019

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A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.

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State-of-the-art review on bridge weigh-in-motion technology

Cited by 190 publications

References 71 publications

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

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