Optimization of structural health monitoring and maintenance planning based on Bayesian joint modeling of time-dependent measurements and hazard functions

Structural Health Monitoring (SHM) provides useful information that reduces the uncertainty in the time-dependent degradation processes of civil structures, and hence facilitates improved decision making. However it comes at a price that is not always justified by its benefit. Therefore, the monitoring and maintenance strategy should be planned in a way that the life-cycle benefit gained by the SHM information is maximized, or at least justified by the cost of SHM. The objective of this paper is to illustrate the evaluation of the value of Structural Health Monitoring (SHM) in the framework of pre-posterior analysis, elaborating the joint modeling of the time-varying structural performance and hazard function in inspection/repair planning and expected total life cycle cost calculation. The hazard function in the joint model is updated by the assumed monitoring outcome, which results in a change of inspection/repair plan and expected Total Life Cycle Cost (TLCC). The difference of expected TLCC is defined as the value of SHM which changes as decision parameter h(t)^T related to the inspection time changes within the maximum acceptable value h_max. By defining the candidate SHM strategies, the problem of optimizing the strategy of monitoring and maintenance can be solved accordingly by identifying the SHM strategy that maximizes the life-cycle benefit of the structure.

The procedure of joint modeling and evaluate the value of SHM is illustrated for a Reinforced Concrete (RC) bridge beam under corrosion according to Bertolini (2008), Marsh & Frangopol (2008) and Stewart (2004). In this case, we consider 6 SHM strategies with increasing accuracy from S1 to S6 and the one, S4, with maximum VoSHM is chosen as optimum SHM strategy. The detail results are listed in Figure 1–2. The expected TLCCs are reduced by implementing SHM strategies S1–S6 due to more information provided. For S1 to S4, the increase of SHM accuracy provides more value than implementing SHM, while the costs for increasing accuracy of S4 to S6 overweigh the value of information provided, thus the VoSHM for S4 to S6 decreases. This cost-effective should be taken into account by the decision maker prior to implementation of SHM in order to maximize the VoSHM. Figure 1 Expected TLCC for S1–S6. https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315207681/cd556cd4-4dcf-4efe-8e29-56fc67b8bfbd/content/fig30_1.tif"/> Figure 2 VoSHM provided by S1–S6. https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315207681/cd556cd4-4dcf-4efe-8e29-56fc67b8bfbd/content/fig30_2.tif"/>