The number of bridges that are approaching or exceeding their initial design life has increased radically. Meanwhile, an ever-increasing volume of traffic each year, both in number and weight of vehicles, is creating an additional critical situation for this kind of structure. To predict the response of bridges to traffic loads and their ultimate capacity with low uncertainties, we can use numerical structural models; however, such uncertainties increase as bridges age due to deterioration mechanisms. Non-destructive tests of material specimens and full-scale on-site load tests of the structure allow to update model parameters and have a better estimate of the bridge behaviour. However, different load tests provide different information with different impacts on the updated model accuracy. With the aid of a real-life case study, the Alveo Vecchio highway bridge, which has been tested to failure with a sequence of progressively increasing load, we aim to understand what behaviour can predict a structural model and what we can learn from a load test. This study is part of a research agreement between the Italian Ministry of Sustainable Infrastructure and Mobility, Autostrade per l’Italia SpA (the main operator of Italian highways), and the University of Trento. It concerns the management and monitoring of civil infrastructure intending to develop survey protocols and monitor systems to assess the safety and performance of existing highway bridges.

Comparison between Model Prediction and Measured Response of a Prestressed Concrete Bridge Tested to Failure / Rossi, F.; Brighenti, F.; Verzobio, A.; Tonelli, D.; Zonta, D.; Migliorino, P.. - 253:(2023), pp. 543-553. (Intervento presentato al convegno 10th European Workshop on Structural Health Monitoring, EWSHM 2022 tenutosi a Palermo, Italy nel 4 - 7 July 2022) [10.1007/978-3-031-07254-3_55].

Comparison between Model Prediction and Measured Response of a Prestressed Concrete Bridge Tested to Failure

Rossi F.;Brighenti F.;Verzobio A.;Tonelli D.;Zonta D.;
2023-01-01

Abstract

The number of bridges that are approaching or exceeding their initial design life has increased radically. Meanwhile, an ever-increasing volume of traffic each year, both in number and weight of vehicles, is creating an additional critical situation for this kind of structure. To predict the response of bridges to traffic loads and their ultimate capacity with low uncertainties, we can use numerical structural models; however, such uncertainties increase as bridges age due to deterioration mechanisms. Non-destructive tests of material specimens and full-scale on-site load tests of the structure allow to update model parameters and have a better estimate of the bridge behaviour. However, different load tests provide different information with different impacts on the updated model accuracy. With the aid of a real-life case study, the Alveo Vecchio highway bridge, which has been tested to failure with a sequence of progressively increasing load, we aim to understand what behaviour can predict a structural model and what we can learn from a load test. This study is part of a research agreement between the Italian Ministry of Sustainable Infrastructure and Mobility, Autostrade per l’Italia SpA (the main operator of Italian highways), and the University of Trento. It concerns the management and monitoring of civil infrastructure intending to develop survey protocols and monitor systems to assess the safety and performance of existing highway bridges.
2023
Lecture Notes in Civil Engineering
Singapore
Springer Science and Business Media Deutschland GmbH
978-3-031-07253-6
978-3-031-07254-3
Rossi, F.; Brighenti, F.; Verzobio, A.; Tonelli, D.; Zonta, D.; Migliorino, P.
Comparison between Model Prediction and Measured Response of a Prestressed Concrete Bridge Tested to Failure / Rossi, F.; Brighenti, F.; Verzobio, A.; Tonelli, D.; Zonta, D.; Migliorino, P.. - 253:(2023), pp. 543-553. (Intervento presentato al convegno 10th European Workshop on Structural Health Monitoring, EWSHM 2022 tenutosi a Palermo, Italy nel 4 - 7 July 2022) [10.1007/978-3-031-07254-3_55].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/393189
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