Abstract
Efficient life-cycle management of civil infrastructure systems under continuous deterioration can be improved by studying the sensitivity of optimised preventive maintenance decisions with respect to changes in model parameters. Sensitivity analysis in maintenance optimisation problems is important because if the calculation of the cost of preventive maintenance strategies is not sufficiently robust, the use of the maintenance model can generate optimised maintenances strategies that are not cost-effective. Probabilistic sensitivity analysis methods (particularly variance based ones), only partially respond to this issue and their use is limited to evaluating the extent to which uncertainty in each input contributes to the overall output's variance. These methods do not take account of the decision-making problem in a straightforward manner. To address this issue, we use the concept of the Expected Value of Perfect Information (EVPI) to perform decision-informed sensitivity analysis: to identify the key parameters of the problem and quantify the value of learning about certain aspects of the life-cycle management of civil infrastructure system. This approach allows us to quantify the benefits of the maintenance strategies in terms of expected costs and in the light of accumulated information about the model parameters and aspects of the system, such as the ageing process. We use a Gamma process model to represent the uncertainty associated with asset deterioration, illustrating the use of EVPI to perform sensitivity analysis on the optimisation problem for age-based and condition-based preventive maintenance strategies. The evaluation of EVPI indices is computationally demanding and Markov Chain Monte Carlo techniques would not be helpful. To overcome this computational difficulty, we approximate the EVPI indices using Gaussian process emulators. The implications of the worked numerical examples discussed in the context of analytical efficiency and organisational learning.
Highlights
The cost effective life-cycle management of civil infrastructure systems is highly dependent on the determination of optimal maintenance and rehabilitation strategies
Preventative Maintenance (PM) is widely used to mitigate asset deterioration and reduce the risk of unexpected failure and as a strategy can be sub-classified into two approaches; time-based maintenance (TBM), where maintenance activities take place at predetermined time intervals, and condition-based maintenance (CBM) where interventions are prompted by information collected through condition sensing and monitoring processes
We provide a holistic approach for guiding making optimised decisions in the presence of uncertainty using value of information analysis
Summary
The cost effective life-cycle management of civil infrastructure systems is highly dependent on the determination of optimal maintenance and rehabilitation strategies. Preventive maintenance strategies (both time and condition based) are widely used for infrastructure life-cycle management decision making. These strategies can be planned and scheduled and their costs. The central challenge for those who wish to make informed PM decisions is that determining the time to first inspection, maintenance intervention, or replacement is confounded by model parameter uncertainties associated with the adopted failure, deterioration, repair, or maintenance model. The determination of EVPPI involves the calculation of multidimensional integrals that are often computationally demanding, making conventional numerical integration or Monte Carlo simulation techniques infeasible in practice To partially overcome this computational difficulty, we follow the work of [7,8], and execute SA through the use of Gaussian process emulators. We conclude by discussing the implications of our approach and identify opportunities for future work
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