The paper describes an integrated procedure for evaluation of the service-life curve of existing bridges, calibrated with data derived from visual inspections and aimed at assessing the degree of deterioration of their structural/non-structural elements. Visual inspections allowed a total sufficiency rating for the bridges to be calculated, together with data collection, to be used in Bayesian updating of element deterioration curves. The results were combined with information from simulations of potential bridge deterioration scenarios to quantify mean trends and related uncertainties of the remaining service-life curves of existing bridges. The proposed procedure was applied to two bridge stocks and results are critically discussed. The method used may allow public authorities/private managing companies to optimise economic resources allocated to long/medium-term maintenance plans for the most critical structures.
Bridge residual service-life prediction through Bayesian visual inspection and data updating
ZANINI, MARIANO ANGELO;FALESCHINI, FLORA;PELLEGRINO, CARLO
2017
Abstract
The paper describes an integrated procedure for evaluation of the service-life curve of existing bridges, calibrated with data derived from visual inspections and aimed at assessing the degree of deterioration of their structural/non-structural elements. Visual inspections allowed a total sufficiency rating for the bridges to be calculated, together with data collection, to be used in Bayesian updating of element deterioration curves. The results were combined with information from simulations of potential bridge deterioration scenarios to quantify mean trends and related uncertainties of the remaining service-life curves of existing bridges. The proposed procedure was applied to two bridge stocks and results are critically discussed. The method used may allow public authorities/private managing companies to optimise economic resources allocated to long/medium-term maintenance plans for the most critical structures.Pubblicazioni consigliate
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