Bridge fatigue assessment and management using reliability-based crack growth and probability of detection models

Abstract

This paper focuses on conducting lifetime performance assessment and management of aging steel bridges under fatigue by integrating three prediction models: fatigue reliability model (FRM), crack growth model (CGM), and probability of detection (PoD) model. A novel approach using these models is proposed for planning interventions on fatigue sensitive structures. Based on information from field monitoring and/or non-destructive evaluation, prediction models are developed to (a) estimate the time-dependent fatigue performance using FRM, (b) provide the time-dependent crack growth using CGM, and (c) quantify the detection capability associated with fatigue cracks using PoD model. In order to assess and manage bridge fatigue life, the three models are combined based on two parameters (i.e., number of cycles, crack size). As an illustration, the combined approach is used for fatigue assessment and management of an existing bridge.