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

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.