Incorporating defect specific condition indicators in a bridge life cycle analysis

Bridges are critical assets for the safe, reliable and functional operation of transportation networks. Infrastructure asset managers are responsible for ensuring that these bridges adhere to rigorous safety standards using the finite resources available to transportation agencies. To facilitate strategy development and to present decisions to stakeholders, a life cycle analysis is commonly performed. Many bridge owners use stochastic models that are calibrated using condition records from visual examinations, however, condition records typically report bridge condition on a single condition scale. In this study, defect specific condition scales are utilised to implement multiple defect specific condition indicators in the modelling of deterioration. These additional indicators enable the modelling of the interactions between defects during deterioration. Moreover, the indicators are used in the modelling of different defect specific maintenance interventions providing the scope to quantitatively assess the effects of strategies that favour early intervention. A multiple defect deterioration model is presented as a dynamic Bayesian network, which is calibrated using records for metallic girders from railway bridges in the United Kingdom. A Petri net model is then used to perform a life cycle analysis, which incorporates a novel dynamic conditional approach for Petri net modelling to utilise the multiple condition indicators.