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Service life estimate is crucial for evaluating the economic and environmental sustainability of projects, by means—adopting a life cycle perspective—of the Life Cycle Cost Analysis (LCCA). Service life, in turn, is strictly correlated to maintenance investment and planning activities, in view of building/building component/system/infrastructure products’ durability requirements, and in line with the environmental-energy policies, transposed into EU guidelines and regulations. Focusing on the use-maintenance-adaptation stage in the constructions’ life cycle, the aim of this work is to propose a methodology for supporting the “optimal maintenance planning” in function of life cycle costs, assuming the presence of financial constraints. A first research step is proposed for testing the economic sustainability of different project options, at the component scale, which imply different cost items and different maintenance-replacement interventions over time. The methodology is based on the Annuity Method, or Equivalent Annual Cost approach, as defined by the norm EN 15459-1:2017. The method, poorly explored in the literature, is proposed here as an alternative to the Global Cost approach (illustrated in the norm as well). Due to the presence of uncertainty correlated to deterioration processes and market variability, the stochastic Annuity Method is modeled by introducing flexibility in input data. Thus, with the support of Probability Analysis and the Monte Carlo Method (MCM), the stochastic LCCA, solved through the stochastic Equivalent Annual Cost, is used for the economic assessment of different maintenance scenarios. Two different components of an office building project (a timber and an aluminum frame), are assumed as a case for the simulation. The methodology intends to support decisions not only in the design phases, but also in the post-construction ones. Furthermore, it opens to potential applications in reinforced concrete infrastructures’ stock, which is approaching, as a considerable portion of the building stock, its end-of-life stage.

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The Stochastic Annuity Method for Supporting Maintenance Costs Planning and Durability in the Construction Sector: A Simulation on a Building Component