In earthquake-prone countries, most of the existing bridges were designed in the past without appropriate anti-seismic regulations and can induce important direct or indirect losses if subjected to severe seismic ground shaking. The main challenges in the extensive seismic risk assessment of existing bridges are related to the large number of structures to be inspected and the limited available resources. Therefore, time- and cost-saving approaches for providing seismic risk metrics on existing bridges are needed. This dissertation investigates efficient methodologies for bridge-specific seismic risk assessment within portfolio analysis by using multi-source data integration and simplified mechanical approaches. A methodology for multi-source data collection is described. The applicability of remote-sensing data in populating inventory for structural analysis purposes is discussed. A procedure for using Remotely Piloted Aircraft Systems and photogrammetry to retrieve exhaustive structural information is presented. The effectiveness of displacement-based assessment approaches to be used together with the capacity spectrum method (CSM) for seismic performance assessment is analysed, considering continuous-deck reinforced-concrete (RC) and steel truss multi-span bridges. A fragility analysis methodology based on cloud analysis using the CSM results is also presented. The CSM is applied with real (i.e. recorded) ground-motion spectra (as opposed to code-based conventional spectra) to explicitly consider record-to-record variability. A seismic risk assessment framework combining the proposed efficient data collection and simplified probabilistic seismic assessment methodologies is finally presented. It accounts for the influence of knowledge-based uncertainties associated with an initial incomplete data collection. The proposed approach is applied and tested on eight simply-supported RC bridges of the Basilicata national road network.

Seismic fragility and risk assessment of large bridge portfolios: efficient mechanical approaches based on multi-source data collection and integration

Nettis, Andrea
2021

Abstract

In earthquake-prone countries, most of the existing bridges were designed in the past without appropriate anti-seismic regulations and can induce important direct or indirect losses if subjected to severe seismic ground shaking. The main challenges in the extensive seismic risk assessment of existing bridges are related to the large number of structures to be inspected and the limited available resources. Therefore, time- and cost-saving approaches for providing seismic risk metrics on existing bridges are needed. This dissertation investigates efficient methodologies for bridge-specific seismic risk assessment within portfolio analysis by using multi-source data integration and simplified mechanical approaches. A methodology for multi-source data collection is described. The applicability of remote-sensing data in populating inventory for structural analysis purposes is discussed. A procedure for using Remotely Piloted Aircraft Systems and photogrammetry to retrieve exhaustive structural information is presented. The effectiveness of displacement-based assessment approaches to be used together with the capacity spectrum method (CSM) for seismic performance assessment is analysed, considering continuous-deck reinforced-concrete (RC) and steel truss multi-span bridges. A fragility analysis methodology based on cloud analysis using the CSM results is also presented. The CSM is applied with real (i.e. recorded) ground-motion spectra (as opposed to code-based conventional spectra) to explicitly consider record-to-record variability. A seismic risk assessment framework combining the proposed efficient data collection and simplified probabilistic seismic assessment methodologies is finally presented. It accounts for the influence of knowledge-based uncertainties associated with an initial incomplete data collection. The proposed approach is applied and tested on eight simply-supported RC bridges of the Basilicata national road network.
2021
Inglese
Uva, Giuseppina
Tarantino, Eufemia
Raffaele, Domenico
Mossa, Michele
Politecnico di Bari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/64383
Il codice NBN di questa tesi è URN:NBN:IT:POLIBA-64383