Quantifying the effects of interventions based on visual inspections from a network of bridges. Issue 8 (3rd August 2022) - Hamida, Zachary; Goulet, James-A.
Abstract: Network-scale maintenance planning mainly depends on the expected improvement in a structure's condition following an intervention. Quantifying the effect of interventions is commonly based on either the expert judgement and reference values, or ad hoc estimation from visual inspection data. However, visual inspections are subjective and have large variability over time due to the inspectors' uncertainty. State-space models (SSM) have been effectively utilised for quantifying the inspectors uncertainty and modelling the deterioration based on visual inspections. In this study, a new method is proposed to quantify the effect of interventions on structures using the SSM framework. This method allows estimating the local effect of interventions at the structural-element level as well as at the network-scale for a population of structural elements. Moreover, the estimation procedure takes into account the inspectors uncertainty and offers a coherent integration for intervention actions within the deterioration model, in addition to enabling the estimation for the service-life of interventions. The proposed approach is verified using synthetic data and validated using real data taken from inspections and interventions performed on a bridge network in Canada.