The current practice of burying utilities in an unsustainable way has led to difficulties in their maintenance, contributing to their poor condition. The process of accessing these utilities through open-cut excavation has negative impacts on the environment and society, which can be measured as social costs incurred over the asset's lifespan. To minimize these costs, it is possible to synchronize the interventions of different assets, such as water and sewer pipes and pavements, in the same street segment. It is important to note that synchronized interventions are not a long-term solution and do not eliminate the need for future excavations. Multi-Purpose Utility Tunnels (MUTs) offer a sustainable option by consolidating all underground utilities into a single accessible tunnel. This placement method eliminates the need for future excavations, associated costs, and negative socioeconomic impacts. However, despite their benefits, both the MUTs and the synchronized interventions are not widely practiced. Therefore, a systematic approach for estimating the lifecycle costs (LCCs) of each alternative is needed. The main objective of this research is to optimize the agency and social LCCs and network deterioration using multi-objective optimization for identifying the potential locations of MUTs. This selection involves modeling the agency and social cost savings and the network deterioration resulting from the implementation of both the synchronized method and the MUT at the network and segment levels. The main contributions developed in the context of this research are:
(1) Defining a multi-objective optimization model that was able to identify the potential MUT locations and a multi-year plan for MUT implementation that offers lifecycle savings;
(2) Developing asystematic method for comparing the results of the MUT optimization with those of the synchronized interventions. This comparison is based on the agency and social LCCs, and the network deterioration generated by the MUTs and synchronized method of utility interventions at the network and segment levels;
(3) Applying three regression models for capturing the social cost of both alternatives at the network and segment levels.
