Measurement of Truck Axle Weights by Instrumenting Longitudinal Ribs of Orthotropic Bridge

Yamada, Kentaro; Inoue, Jirou; Yamaguchi, Kouta

doi:10.1061/(asce)1084-0702(2006)11:5(526)

Cited by 18 publications

(9 citation statements)

References 4 publications

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“…In this study, to detect vehicle entry and exit, rather than the conventional method using a strain gauge, (19) the acceleration measured using an accelerometer at the vertical stiffener at both longitudinal edges of the main girder was used. Figure 14 shows the measured acceleration at both longitudinal edges of the main girder (G C 3) and the measured displacement response of the main girder (G C 3) obtained using a contact displacement gauge.…”

Section: Results For Detection Of Vehicle Entry and Exitmentioning

confidence: 99%

Identification of Cause of Displacement-Induced Fatigue in Steel Bridges Based on Displacement Measurements Using High-Performance Micro-electromechanical Systems Sensor

2017

Sensors and Materials

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It is important to know the displacement response of steel bridges under live loads to ensure that they are properly maintained. In particular, with regard to fatigue damage, if the bridge deformation can be determined based on the displacement response measured at multiple locations, the cause of displacement-induced fatigue can be identified, and appropriate repairs and reinforcements can be implemented as a countermeasure against fatigue damage. In recent years, accelerometers based on micro-electromechanical systems (MEMS) have been proposed to measure the displacement response of bridges to external forces because they are inexpensive and easy to install. However, it is difficult to choose an appropriate accelerometer for this task because the signal-to-noise (S/N) ratio that is required to accurately determine the bridge deflection from the measured acceleration has not been clarified. In the present study, to clarify the dominant frequency range of the displacement of a bridge under live loads, field measurements of two bridges in service were conducted using laser displacement gauges and accelerometers. Next, to clarify the required S/N ratio at the dominant frequency range of bridge displacement, constant-amplitude vibration tests using 10 different accelerometers consisting of nine MEMS accelerometers and a servo-type accelerometer were conducted. By conducting the field measurement of a bridge in service, the bridge displacement responses at multiple points were determined using high-performance MEMS accelerometers. Finally, by analyzing the correlation between the bridge deformation determined from multiple displacement responses obtained using the MEMS accelerometers and the stress at a location where cracks are likely to develop, the cause of displacement-induced fatigue was identified.

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Section: Results For Detection Of Vehicle Entry and Exitmentioning

confidence: 99%

Identification of Cause of Displacement-Induced Fatigue in Steel Bridges Based on Displacement Measurements Using High-Performance Micro-electromechanical Systems Sensor

2017

Sensors and Materials

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“…For the second one-by-one vehicles scenario, GVW of the first front vehicle can still be calculated through (16). Then GVW of the rear vehicles can be calculated after subtracting effects of the front vehicle whose GVW is already known.…”

Section: Field Testsmentioning

confidence: 99%

“…where is the peak value of the rear vehicle induced static strain signal, is the strain influence value related to the position of the front vehicle, which can be obtained with the aid of the aforementioned computer vision technique, is the GVW of the front vehicle calculated through (16), is the peak value of the calibrated strain influence line of traffic lane where the rear vehicle drives, and is the GVW of the rear vehicle.…”

Section: Field Testsmentioning

confidence: 99%

“…It is important to highlight that using (16) to calculate the weight of the front vehicle in the one-by-one vehicle queue is not applicable to circumstances when the rear vehicle enters the bridge before the front vehicle passes the instrumented bridge cross-section, because in (16) involves the effects of the rear vehicle under such circumstances. Fortunately, this problem does not exist in this research; for a sizeable safety margin, no less than 30 m, between front and rear vehicles, is demanded when driving on highways in China.…”

Section: Field Testsmentioning

confidence: 99%

“…Lamentably, these sensors are too costly for its massive installation in actual structures. Alternatively, Xiao et al [16] and Yamaguchi et al [17] innovatively utilized the longitudinal ribs strains of an orthotropic steel bridge to detect the transverse position of vehicle axles. Unfortunately, concrete bridges without ribs are insensitive to single axle loads, making this method ineffective for this kind of structures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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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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Measurement of Truck Axle Weights by Instrumenting Longitudinal Ribs of Orthotropic Bridge

Cited by 18 publications

References 4 publications

Identification of Cause of Displacement-Induced Fatigue in Steel Bridges Based on Displacement Measurements Using High-Performance Micro-electromechanical Systems Sensor

Identification of Cause of Displacement-Induced Fatigue in Steel Bridges Based on Displacement Measurements Using High-Performance Micro-electromechanical Systems Sensor

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

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

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