Bridge weigh-in-motion system development using superposition of dynamic truck/static bridge interaction

Leming, S.K.; Stalford, Harold

doi:10.1109/acc.2003.1239122

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…Previous studies also point out that road roughness and vehicle velocity will affect the GVW recognition accuracy because of the vehicle-bridge coupling vibration. The faster the vehicle drives, the larger the GVW recognition errors are [40]. However, according to the obtained results, this issue is not significant in this research.…”

Section: Statistical Analysis For Identification Resultsmentioning

confidence: 63%

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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Section: Statistical Analysis For Identification Resultsmentioning

confidence: 63%

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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“…The weight estimation methods, in this class of assumption, determine axle weights of the vehicle by comparing the measured bridge responses with those obtained from bridge influence lines (e.g. see Moses 1978, Kriss 1979, Gagarine 1991, WAVE 2001, Leming and Stalford 2003, Rowley et al 2008. The determined individual axle weights are then summed to estimate the gross weight of vehicle.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of effectiveness of vehicle weight estimations using bridge weigh-in-motion

Deesomsuk

Pinkaew

2010

The IES Journal Part A: Civil & Structural Engineering

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The effectiveness of two estimation methods of vehicle weight using bridge weigh-in-motion data is evaluated. The measured bending moments of a bridge under a moving vehicle are numerically simulated and are used as the input for weight estimations. Two axle weight estimation methods assuming constant magnitudes (Method I) and time-varying magnitudes (Method II) of axle loads are investigated and compared. One thousand cases of random values of vehicle-bridge parameters are considered. It is found that the weight estimation errors from both methods become larger as the vehicle speed becomes faster. The combined effect of closely spaced axles with high vehicle speeds is clearly observed. In general, both methods can provide rather accurate weight estimation, i.e. gross weight estimation errors are within +10%. Comparing the two methods, although Method I exhibits a faster computational speed, Method II can provide axle load histories and exhibits more accurate weight estimations for almost all the considered cases.

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“…In this sense, authors like Jiang et al [12], Au et al [13], Law et al [14], Deng and Cai [15], Pan and Yu [16] and Kim et al [17] sought to employ different optimization techniques to solve the problem, from the use of genetic algorithms to artificial neural networks. Besides these systems in time domain, several authors proposed methods that use the bridge dynamic response ( [18], [19], [20], [21] e [22]). Nevertheless, these methods are still quite complex and difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

B-WIM systems application on reinforced concrete bridge structural assessment and highway traffic characterization

Junges

Pinto

Miguel

2017

Rev. IBRACON Estrut. Mater.

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ResumoThe vehicles that travel on Brazilian highways have changed a lot in the last decades, with an increase in the traffic load and in the amount of trucks. This fact is not exclusive to our country, so much that in order to assess the structural safety of bridges, there was a great development in bridge weigh-in-motion systems (B-WIM) the last decade, especially in developed countries. Moses, in 1979, was the first one to introduce the B-WIM concept. This work presents the results of a B-WIM system applied on a bridge over the Lambari river, located at BR 153 in Uruaçu (Goiás). The weigh-in-motion technique used is based on Moses' Algorithm and uses influence lines obtained direct from traffic. Traffic characterization of that particular highway, as well as the effects introduced in the bridge structure and the experimental dynamic amplification factor are also discussed. At the end it is concluded that the system used is capable of detecting, with good precision, the axle spacing and the gross vehicle weight shows errors inferior to 3% when compared with the gross weight acquired with static scale.Keywords: B-WIM, monitoring, traffic characteristics, bridges, safety.Os veículos que trafegam nas rodovias brasileiras mudaram muito nas últimas décadas, ocorrendo um aumento na capacidade de carga e na quantidade de caminhões. Esse fato não é exclusivo do nosso país, tanto que na última década houve um grande desenvolvimento dos sistemas de pesagem em movimento em pontes (bridge weigh-in-motion, B-WIM), especialmente nos países desenvolvidos, para avaliação da segurança estrutural de pontes. Moses em 1979 foi o primeiro a introduzir o conceito de B-WIM e o algoritmo por ele desenvolvido continua sendo o mais popular nos sistemas comerciais. No presente estudo são mostrados os resultados da utilização de um sistema B-WIM no monitoramento de uma ponte sobre o rio Lambari, na BR 153, no município de Uruaçu (Goiás). A técnica de pesagem em movimento empregada é baseada no algoritmo de Moses e utiliza linhas de influência obtidas diretamente do tráfego. A caracterização do tráfego atuante nessa rodovia, bem como os esforços introduzidos na estrutura da ponte e um coeficiente de impacto obtido de forma experimental são também discutidos. Ao final conclui-se que o sistema empregado é capaz de detectar com boa precisão o espaçamento entre os eixos e o peso bruto total dos veículos apresenta erros inferiores a 3% quando comparados com os pesos obtidos em balança estática.Palavras-chave: B-WIM, monitoramento, característica do tráfego, pontes, segurança.

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Bridge weigh-in-motion system development using superposition of dynamic truck/static bridge interaction

Cited by 9 publications

References 3 publications

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

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

Evaluation of effectiveness of vehicle weight estimations using bridge weigh-in-motion

B-WIM systems application on reinforced concrete bridge structural assessment and highway traffic characterization

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