Robust multi-objective maintenance planning of deteriorating bridges against uncertainty in performance model

• We propose a new robust multi-objective maintenance planning method of deteriorating bridges against model uncertainties.
• Simulated system uncertainties are incorporated into the GA-based multi-objective optimization framework.
• The preference-based objective-space reduction method is utilized to enhance searching efficiency.
• Numerical example of a typical prestressed concrete girder bridge is provided.
• Proposed method produces well-balanced maintenance strategy both in terms of bridge performance and maintenance cost.

This study proposes a new robust multi-objective maintenance planning approach of the deteriorating bridges against uncertainty in performance degradation model. The main focus is to guarantee the performance requirements of the bridge by the scheduled maintenance interventions even in the presence of uncertainty in time-dependent performance degradation model. The uncertainties are modeled as the perturbation of the system parameters. These are simulated by a sampling method, and incorporated into the GA-based multi-objective optimization framework which produces a set of optimal preventive maintenance scenarios. In order to focus the searching on the most preferable region, the performance models of the bridge components are all integrated into single overall performance measure by using the preference-based objective-space reduction method. Numerical example of a typical prestressed concrete girder bridge is provided to demonstrate the new robust maintenance scheduling approach. For comparison purpose, non-robust multi-objective maintenance planning without considering uncertainty of the bridge performance is also provided. It is verified that the proposed approach can produce successfully-performing maintenance scenarios under the perturbation of bridge condition grades while maintaining well-balanced maintenance strategy both in terms of bridge performance and maintenance cost.