Kielhauser, Clemens; Adey, Bryan T. (June 2017). A Methodology to Determine Optimal Intervention Programs for Multiple Urban Infrastructure Networks with a Consistent Representation of Service Levels In: 2nd International Symposium on Infrastructure Asset Management (SIAM 2017). Zurich, Switzerland. June 29-30, 2017. 10.3929/ethz-b-000171368
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The determination of optimal intervention programs for municipal infrastructure networks, such as road, gas and water distribution networks is a challenging task. To find optimal intervention programs, it is necessary to take into consideration not only 1) the costs of interventions in terms of owner costs, but also 2) the potential loss of service both before, during and after the execution of the interventions, e.g. a failure due to differential loading of underground of pipes, and 3) the costs due to the loss of service when interventions are being executed, e.g. the consequences related to falls in water pressure or the consequences of a stoppage of electric current. As municipalities consist of multiple overlapping networks, it is also necessary to take into consideration that: 1) reductions in costs of intervention are possible when interventions are executed on multiple networks simultaneously, e.g. the replacement of the water and sewer pipes under one road are lower if they are done together than separately because the road only has to be opened once, and 2) interventions on one network can increase the probability of failure of another network, e.g. a backhoe can accidentally hit the water distribution pipe if it is being used to remove the soil necessary to excavate the gas pipe. In this paper, a methodology is presented that can be used to determine optimal intervention programs for multiple urban infrastructure networks simultaneously, taking into consideration these issues. Special emphasis is made on the new generic model for loss of service that can be used for all networks. The methodology is then critically discussed and recommendations for future work are given.