• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.2
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• pp.103-111
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• 2000

The objectives of the study are to know the strain distribution and modal dynamic behaviour of steel bridge girders by actual traffic load. The live load effect depends on many parameters including the span length, gross vehicle weight, axle weight, axle configuration so on. For the estimation of static and dynamic characteristic, strain data caused by moving loads and traffic characteristics of passing vehicle under actual traffic load have measured using Bridge Weigh in Motion. To confirm the reliability of BWIM system, strain data measured using the $120{\Omega}$ strain gauge under the same condition. It is considered that the data acquired from BWIM system have reliability through the analysis and comparison between stress measured by strain data from BWIM and computed by FEM. Additionally according to the measured strain data of up-line and down-line on the highway, the up-line bridge grows more faster than the down-line bridge and girder 4 and 5 carry more load when vehicles pass the inner line and girder 2 and 3 does when vehicles pass the outer line as this case(the bridge composed with 5 girders).

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.10-18
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• 2024

Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.3
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• pp.233-245
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• 2011

A new BWIM(bridge weigh-in-motion) algorithm using both measured strain and acceleration data is proposed. To consider the effects of bridge vibration on the estimation of moving loads, the dynamic governing equation is applied with the known stiffness and mass properties but damping is ignored. Dynamic displacements are computed indirectly from the measured strains using the beam theory and accelerations are measured directly by accelerometers. To convert a unit moving load to its equivalent nodal force, a transformation matrix is determined. The incompleteness in the measured responses is considered in developing the algorithm. To examine the proposed BWIM algorithm, simulation studies, laboratory experiments and field tests were carried. In the simulation study, effects of measurement noise and estimation error in the vehicle speed on the results were investigated.

• Proceedings of the KSR Conference
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• 2002.10b
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• pp.1074-1079
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• 2002

Long term measurement data on the bridge response caused by moving loads are fundamental ingredient to the development or improvement of the new bridge design. In addition, proper establishment of the systematic analysis and diagnosis together with the maintenance system become the essential procedure to the effective repair/reinforcement/retrofit of not only the high speed but also the conventional railway bridges. Therefore, the real time health monitoring system on the important railway bridges should be enhancing the proper maintenance of the structures. The main objective of this study is, therefore, to develop a monitoring device including Weigh-In-Motion (WIM) function and the emphasis is place on the easy and economic installation of the developed system in the field condition.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.79-85
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• 2011

Bridge Weigh-in-Motion(BWIM) system calculates a travelling vehicle's weight without interruption of traffic flow by analyzing the signals that are acquired from various sensors installed in the bridge. BWIM system or data accumulated from the BWIM system can be utilized to development of updated live load model for highway bridge design, fatigue load model for estimation of remaining life of bridges, etc. Field test with moving trucks including various load cases should be performed to guarantee successful development of precise BWIM system. In this paper, a numerical simulation technique is adopted as an alternative or supplement to the vehicle traveling test that is indispensible but expensive in time and budget. The constructed numerical model is validated by comparison experimentally measured signal with numerically generated signal. Also vehicles with various dynamic characteristics and travelling conditions are considered in numerical simulation to investigate the variation of bridge responses. Considered parameters in the numerical study are vehicle velocity, natural frequency of the vehicle, height of entry bump, and lateral position of the vehicle. By analyzing the results, it is revealed that the lateral position and natural frequency of the vehicle should be considered to increase precision of developing BWIM system. Since generation of vehicle travelling signal by the numerical simulation technique costs much less than field test, a large number of test parameters can effectively be considered to validate the developed BWIM algorithm. Also, when artificial neural network technique is applied, voluminous data set required for training and testing of the neural network can be prepared by numerical generation. Consequently, proposed numerical simulation technique may contribute to improve precision and performance of BWIM systems.

• Structural Monitoring and Maintenance
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• v.10 no.3
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• pp.207-220
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• 2023

The safety of bridges are critical in our transportation infrastructure. Bridge design and analysis require complex structural analysis procedures to ensure their safety and stability. One common method is to calculate the maximum moment in the girders to determine the appropriate bridge section. Girder distribution factors (GDFs) provide a simpler approach for performing this analysis. A GDF is a ratio between the response of a single girder and the total response of all girders in the bridge. This paper explores the significance of GDFs in bridge analysis and design, including their importance in the evaluation of existing bridges. We utilized Bridge Weigh-in-motion (B-WIM) measurements of five simple supported girder bridge in Indonesia to develop a simple GDF provisions for the Indonesia's bridge design code. The B-WIM measurements enable us to know each girder strain as a response due to vehicle loading as the vehicle passes the bridge. The calculated GDF obtained from the B-WIM measurements were compared with the code-specified GDF and the American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) bridge design specification. Our study found that the code specified GDF was adequate or conservative compared to the GDF obtained from the B-WIM measurements. The proposed GDF equation correlates well with the AASHTO LRFD bridge design specification. Developing appropriate provisions for GDFs in Indonesian bridge design codes can provides a practical solution for designing girder bridges in Indonesia, ensuring safety while allowing for easier calculations and assessments based on B-WIM measurements.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.261-268
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• 2001

Highway bridges are important infrastructures for national transportation systems. However, due to overweight trucks frequently moving on highways, highway structures have been gradually damaged and economical loss has grown severe. To maintain a highway bridge safe and sound, properties and loading characteristics of passing trucks on the bridge must be evaluated WIM(Weigh-In-Motion) systems have been developed by many research groups for multiple purposes. Most of the developed WIM systems have applied moment influence lines to identify loading characteristics. Since passing trucks are dynamic loads, however, the identified loads by the influence lines for static loads cannot represent the actual situation correctly. The current research investigates the effects of problem of different loading characteristics on load identification and proposes a new algorithm using the concept of moment influence surface. A numerical simulation study is carried out.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.761-769
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• 2017

Accurately extracting the axle distribution information of a passing vehicle from bridge dynamic responses experiences a key and challenging step in non-pavement bridge weigh-in-motion (BWIM). In this article, the wavelet transformation is adopted and the wavelet coefficient curve is used as a substitute for dynamic response. The driving frequency is introduced and expanded to multi-axle vehicle, and the wavelet coefficient curve on specific scale corresponding to the driving frequency is confirmed to contain obvious axle information. On this basis, an automatic method for axle distribution information identification is proposed. The specific wavelet scale can be obtained through iterative computing, and the false peaks due to bridge vibration can be eliminated through cross-correlation analysis of the wavelet coefficients of two measure points. The integrand function that corresponds to the maximum value of the cross-correlation function is used to identify the peaks caused by axles. A numerical application of the proposed axle information identification method is carried out. Numerical results demonstrate that this method acquires precise axle information from the responses of an axle-insensitive structure (e.g., girder) and decreases the requirement of sensitivity structure of BWIM. Finally, an experimental study on a full-scale simply supported bridge is also conducted to verify the effectiveness of this method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.469-475
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• 2019

A long-term bridge monitoring system has been introduced and is under operation for long-term safety management of the structure. However, it is difficult to assess the condition of the quantitative structural system as it only measures responses and does not measure input loads. To overcome these shortcomings, FBG (Fiber Bragg Grating)-based input load measurement sensors were developed in this paper for measuring highway bridge input loads and their validity was verified through laboratory tests.

• Journal of Korean Society of Steel Construction
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• v.26 no.4
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• pp.251-262
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• 2014

It has been considered that factors affecting accuracy of the estimated weight of moving vehicle by BWIM system are vehicle and bridge characteristics, and measurement conditions which is related to the strain curve. In this study, theoretical review and field test were performed to evaluate effect of these factors in BWIM system. From these evaluations, we proposed a way to improve accuracy of the estimated vehicle information in BWIM system. As the results, it was known that girder type and continuity of spans in bridge are not governing factor, but its plane shape gives large influence on accuracy of the estimated vehicle information. In addition, running speed of vehicle has also large effect on the estimated accuracy of axle distance if the distance between second and third axles is short. However, weight sum of the two axles can be estimated reasonably by assuming them as one axle.