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Load rating of an in-service bridge integrating Bridge Weigh-in-Motion data and load test results

 Load rating of an in-service bridge integrating Bridge Weigh-in-Motion data and load test results
Auteur(s): , ,
Présenté pendant IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024, publié dans , pp. 1065-1070
DOI: 10.2749/sanjose.2024.1065
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Old bridges subjected to current service loads are affected by the increased traffic flow and higher load-carrying capacity of new trucks. For structural assessment, knowing the actual loads over t...
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Détails bibliographiques

Auteur(s): (Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia)
(Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia)
(Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024
Publié dans:
Page(s): 1065-1070 Nombre total de pages (du PDF): 6
Page(s): 1065-1070
Nombre total de pages (du PDF): 6
DOI: 10.2749/sanjose.2024.1065
Abstrait:

Old bridges subjected to current service loads are affected by the increased traffic flow and higher load-carrying capacity of new trucks. For structural assessment, knowing the actual loads over the bridge is crucial to obtain realistic results. Bridge Weigh-in-Motion (B-WIM) system facilitates decisions such as posting, construction of a new bridge, reinforcement, or rehabilitation of the structure since it is possible to know the actual loads. In this study, B-WIM data and load tests results are integrated to perform the load rating of an in-service bridge, located in Cali, Colombia. The bridge has prestressed girders with severe deterioration. The sensors deployed are strain gauges and a video camera for speed and axle identification, and for vehicle weight estimation. Data were analyzed for a critical load case and a strain threshold was proposed to trigger possible overloading events. Distribution factors, dynamic amplification factors, and the live-load factor were analyzed for in-service data. Important variations in the load rating factor were observed using in-service data.