A metaheuristic framework to minimize carbon footprint in the maintenance of aging infrastructures

The minimization of carbon footprints of intensive human activities is an essential part of meeting global goals on the climate crisis. In the construction industry, maintenance and dismantling phases of transport infrastructures are two key processes affecting environmental impacts, given the massive material and energetic resources involved. As societies grow larger in size, infrastructures continue to establish themselves as capital assets for the organization of any human activity. Accordingly, when designing maintenance strategies for a vast portfolio of aging structures, a decision-maker needs to ensure adequate levels of safety while addressing the requirements of all the stakeholders involved, accounting for service interruptions, costs, and workforce availability. The core of this work is the design of a comprehensive optimization-driven decision method to schedule maintenance activities on a portfolio of aging structures. This algorithmic methodology aims to minimize the total carbon footprint of the maintenance and dismantling operations while ensuring reliable safety levels and considering the availability of economic and job resources. To this end, this project integrates stochastic simulations into an advanced meta-heuristic framework to correctly model reliability levels and design robust solutions that account for the randomness that arises in complex real-world scenarios. Indeed, the first line of development uses a two-staged Monte Carlo simulation process that yields an estimation of structural reliability for each of the maintenance configurations explored. This approach substantially extends deterministic or fixed time-based evaluations. Subsequently, this strategy is embedded within a metaheuristic framework that efficiently explores the solution space by exploiting the information recorded during its execution. Given a portfolio of target infrastructures, the main result of the proposed methodology is the achievement of optimized maintenance schedules that minimize carbon emissions, ensure the required safety levels, and respect the resource restrictions affecting the decision-maker.