Value of Monitoring in Asset Management: A Social Cost-benefit Analysis Approach
Zouch, M.
Courage, W.
Napoles, O.M.
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How to Cite

Zouch M., Courage W., Napoles O., 2013, Value of Monitoring in Asset Management: A Social Cost-benefit Analysis Approach, Chemical Engineering Transactions, 33, 379-384.
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Abstract

We present a framework to investigate new monitoring techniques for infrastructures and assess their potential value for the network management. This framework is based on a social cost benefit analysis tool that aims to (i) assist decision makers in selecting and developing cost-effective new monitoring techniques and (ii) provide managers with socially optimal maintenance and rehabilitation strategies that take into account output from these monitoring systems. Potential value of monitoring consists mainly in enabling condition-based strategies and providing more accurate and relevant information that should result in more cost-effective strategies. Monitoring provides information about either the structure degradation level or its environment. The condition of the structure is represented by a set of technical performance indicators that reflect its degradation level and are linked to a set of end-user service levels. Finally, the end-user service levels are valuated to optimize the cost and benefits of maintenance and rehabilitation strategies. Main feature of the tool we develop is to enable optimal, dynamic and reliability-based decisions that are reviewed and updated every time a new relevant information is available. Transition probabilities to predict future deterioration levels are estimated and updated using monitoring data to assess risks and optimize its expected cost. Moreover, the derived strategies are socially optimal and take into account indirect impacts of degradations and M&R strategies on the society and the environment. This is done by consideration and valuation of end-user service levels. We use Markov decision processes which are an appropriate framework for decision-making under uncertainty to incorporate reliability and risk measures within the optimization problem.
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