Probabilistic multi-objective optimum combined inspection and monitoring planning and decision making with updating

Abstract

This paper presents a novel probabilistic approach for optimum combined inspection and monitoring planning (CIMP) with updating. This optimum planning is based on five objectives considering life-cycle cost, extended service life, maintenance delay, damage detection delay, and time-based probability of failure. The five-objective optimization provides a Pareto front. Through decision making process, the best solution is selected from the Pareto front. This solution indicates the optimum inspection application time, monitoring application time, and monitoring duration. When the information related to the damage propagation model is obtained from inspection and monitoring, the probabilistic parameters related to damage propagation prediction are updated. Using the updated damage propagation, the objective function is re-formulated and CIMP is re-optimized. The Pareto fronts associated with inspection planning, monitoring planning, and CIMP are compared when the multi-objective optimizations include or exclude the expected monetary loss due to structural failure. The effects of the weights of objectives and the expected monetary loss on the optimum CIMP are investigated. Furthermore, the parameters and damage propagation updated with the information from inspection and monitoring are compared. The proposed approach is applied to an existing fatigue-sensitive steel bridge.

KEYWORDS:

• Decision making
• inspection
• life-cycle cost
• maintenance
• monitoring
• multi-objective optimization
• probability
• service life
• updating

Acknowledgements

The support by grants from: (a) the National Science Foundation (NSF) Award CMMI-1537926, (b) the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA) and (c) the National Research Foundation of Korea (NRF) by Ministry of Science and ICT of Korean government Award NRF-2018R1C1B5044084 is gratefully acknowledged. The opinions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.

Disclosure statement

No potential conflict of interest was reported by the authors.