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Integral analysis and optimization of monitoring and inspection in aging structures: a Bayesian network approach

Subject Area Structural Engineering, Building Informatics and Construction Operation
Applied Mechanics, Statics and Dynamics
Term from 2012 to 2018
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 229790985
 
Final Report Year 2020

Final Report Abstract

Deterioration processes in engineering structures lead to a reduction of service life and can affect the safety of the structure, the environment and people. To reduce the uncertainty in the deterioration process, inspection and monitoring are common practice in most engineering systems. The maintenance costs can however become high if not properly planned. This project focused first on developing a framework for following the evolution of a system reliability, as information is collected during its lifetime. A hierarchical Bayesian network (BN) model forms the basis of that framework. It models the probabilistic relationship between the components of a system, their deterioration process, and the performance of that system. Exact Bayesian inference is computationally powerful tool to include information from the components and reflect their impact on the system. In a second phase, the framework incorporates the optimization of inspection and maintenance strategies. The optimization was first carried out at component level without considering the component interactions. The component interdependence was then taken into account through the DBN framework, and the optimization performed exhaustively over a small number of strategies. Finally, the formulation of the problem in a restricted and parametrized strategy space allows for the implementation of powerful stochastic optimization methods, such as the cross entropy method combined with a noisy function approximator, such as kriging. This heuristic parametrization was outlined and illustrated. The choice of heuristics has been investigated at different levels. First, a component prioritization parameter was introduced, as a proxy to the value of information (VoI) of inspecting a certain component in a multi-component system. It demonstrated that the heuristic approach can model complex decision strategies and capture local effects in a set parameter. Secondly, the efficiency of the heuristic approach was analysed formally in a sequential decision problem for which the exact solution was available. Finally, incorporating continuous monitoring data has been the focus. Notably, the framework developed was utilized to model the information propagation from the components’ monitoring data to the system’s reliability, and evaluate the VoI of a monitoring system using the heuristic approach.

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