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Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets

机译：针对基础设施资产恶化的最佳预防性维护策略的概率敏感性分析

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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.
机译：通过研究优化的预防性维护决策相对于模型参数变化的敏感性，可以改善在持续恶化的情况下民用基础设施系统的有效生命周期管理。维护优化问题中的灵敏度分析非常重要，因为如果预防性维护策略的成本计算不够稳健，则维护模型的使用可能会产生成本效益不高的优化维护策略。概率敏感性分析方法（尤其是基于方差的方法）仅部分响应此问题，其使用仅限于评估每个输入的不确定性对整体输出的方差造成影响的程度。这些方法没有以直接的方式考虑决策问题。为了解决此问题，我们使用“完美信息的期望值”（EVPI）的概念来进行基于决策的敏感性分析：确定问题的关键参数并量化学习生命周期管理某些方面的价值民用基础设施系统。这种方法使我们能够根据预期成本并根据有关模型参数和系统各个方面（例如老化过程）的累积信息来量化维护策略的收益。我们使用Gamma过程模型来表示与资产恶化相关的不确定性，说明了使用EVPI对基于年龄和基于状况的预防性维护策略的优化问题进行敏感性分析。 EVPI指数的评估对计算要求很高，而马尔可夫链蒙特卡洛技术将无济于事。为了克服这一计算难题，我们使用高斯过程仿真器对EVPI指数进行了近似。在分析效率和组织学习的背景下讨论的工作数值示例的含义。

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