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A platform for research: civil engineering, architecture and urbanism
Estimating bridge criticality due to extreme traffic loads in highway networks
https://orcid.org/0000-0001-8488-1447
https://orcid.org/0000-0001-5405-0626
https://orcid.org/0000-0002-6764-4674
Abstract Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to reduce the risk of collapsing, damage to infrastructure, and economic losses. This paper proposes a methodology to quantify the structural criticality of bridges at a network level. To accomplish this, long-run site-specific simulations are conducted using Bayesian Networks and bivariate copulas, utilizing recorded traffic data obtained from permanent counting stations. To enhance the dataset, information from Weigh-in-Motion systems from different regions was integrated through a matching process. Subsequently, the structural response resulting from the simulated traffic is assessed, and the extreme values of the traffic load effects are obtained for selected return periods. Site-specific bridge criticality as a performance indicator for traffic load effects is derived by comparing the extreme load effects with the design load effects. The outcomes are mapped to facilitate visualization employing an open-source geographic information system application. To illustrate the application of the methodology, a total of 576 bridges within a national highway network are investigated, and a comparison with a popular simplified method is shown. The methodology herein presented can be used to assist in assessing the condition of a bridge network and prioritizing maintenance and repair activities by identifying potential bridges subjected to major load stress.
Highlights Methodology for mapping extreme load effects due to heavy vehicles in bridge networks Evaluation of bridge criticality as a performance indicator for traffic load effects Simple yet robust method using Bayesian Networks and copulas for traffic simulation Application of the methodology to Mexico’s national bridge network as case study
Estimating bridge criticality due to extreme traffic loads in highway networks
https://orcid.org/0000-0001-8488-1447
https://orcid.org/0000-0001-5405-0626
https://orcid.org/0000-0002-6764-4674
Abstract Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to reduce the risk of collapsing, damage to infrastructure, and economic losses. This paper proposes a methodology to quantify the structural criticality of bridges at a network level. To accomplish this, long-run site-specific simulations are conducted using Bayesian Networks and bivariate copulas, utilizing recorded traffic data obtained from permanent counting stations. To enhance the dataset, information from Weigh-in-Motion systems from different regions was integrated through a matching process. Subsequently, the structural response resulting from the simulated traffic is assessed, and the extreme values of the traffic load effects are obtained for selected return periods. Site-specific bridge criticality as a performance indicator for traffic load effects is derived by comparing the extreme load effects with the design load effects. The outcomes are mapped to facilitate visualization employing an open-source geographic information system application. To illustrate the application of the methodology, a total of 576 bridges within a national highway network are investigated, and a comparison with a popular simplified method is shown. The methodology herein presented can be used to assist in assessing the condition of a bridge network and prioritizing maintenance and repair activities by identifying potential bridges subjected to major load stress.
Highlights Methodology for mapping extreme load effects due to heavy vehicles in bridge networks Evaluation of bridge criticality as a performance indicator for traffic load effects Simple yet robust method using Bayesian Networks and copulas for traffic simulation Application of the methodology to Mexico’s national bridge network as case study
Estimating bridge criticality due to extreme traffic loads in highway networks
https://orcid.org/0000-0001-8488-1447
https://orcid.org/0000-0001-5405-0626
https://orcid.org/0000-0002-6764-4674
Abstract Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to reduce the risk of collapsing, damage to infrastructure, and economic losses. This paper proposes a methodology to quantify the structural criticality of bridges at a network level. To accomplish this, long-run site-specific simulations are conducted using Bayesian Networks and bivariate copulas, utilizing recorded traffic data obtained from permanent counting stations. To enhance the dataset, information from Weigh-in-Motion systems from different regions was integrated through a matching process. Subsequently, the structural response resulting from the simulated traffic is assessed, and the extreme values of the traffic load effects are obtained for selected return periods. Site-specific bridge criticality as a performance indicator for traffic load effects is derived by comparing the extreme load effects with the design load effects. The outcomes are mapped to facilitate visualization employing an open-source geographic information system application. To illustrate the application of the methodology, a total of 576 bridges within a national highway network are investigated, and a comparison with a popular simplified method is shown. The methodology herein presented can be used to assist in assessing the condition of a bridge network and prioritizing maintenance and repair activities by identifying potential bridges subjected to major load stress.
Highlights Methodology for mapping extreme load effects due to heavy vehicles in bridge networks Evaluation of bridge criticality as a performance indicator for traffic load effects Simple yet robust method using Bayesian Networks and copulas for traffic simulation Application of the methodology to Mexico’s national bridge network as case study
Estimating bridge criticality due to extreme traffic loads in highway networks
Mendoza-Lugo, Miguel Angel (author) / Nogal, Maria (author) / Morales-Nápoles, Oswaldo (author)
Engineering Structures ; 300
2023-11-12
Article (Journal)
Electronic Resource
English
Estimating bridge criticality due to extreme traffic loads in highway networks
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