Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks

Abstract

In most structural systems, it is neither possible nor optimal to inspect all system components regularly. An optimal inspection-repair strategy controls deterioration in structural systems efficiently with limited cost and acceptable reliability. At present, an integral risk-based optimization procedure for entire structural systems is not available; existing risk-based inspection methods are limited to optimizing inspections component by component. The challenges to an integral approach lie in the large number of optimization parameters in the inspection-repair process of a structural system, and the need to perform probabilistic inference for the entire system at once to address interdependencies among all components. In this paper, we outline a methodology for an integral risk-based optimization of inspections in structural systems, which utilizes a heuristic approach to formulating the optimization problem. It takes basis in a recently developed dynamic Bayesian network (DBN) framework for rapid computation of the system reliability conditional on inspection results. The optimization problem is solved by nesting the DBN inside a Monte-Carlo simulation for computing the expected cost associated with a system-wide inspection strategy. The proposed methodology is applied to a structural system subject to fatigue deterioration and it is demonstrated that it enables an integral risk-based inspection planning for structural systems.