Markov Chain Modeling for Predicting the Service Life of Buildings and Structural Components

Accurate prediction and management of the service life of buildings and structural components are crucial for ensuring durability and economic efficiency. This paper investigates both discrete- and continuous-time Markov chains as probabilistic models for representing deterioration processes of building structures. Transition probabilities, fundamental matrices, and absorption times are computed to quantify expected lifespans and degradation pathways. Numerical simulations illustrate how state probabilities evolve, inevitably converging toward structural failure in the absence of maintenance interventions. Additionally, this study explicitly addresses uncertainties inherent in lifecycle predictions through the application of fuzzy set theory. A fuzzy Markov chain model is formulated to represent imprecise deterioration states and transition probabilities, which validate the predictable yet uncertain progression of structural deterioration through graphical analyses and fuzzy simulations. The proposed methodology, including fuzzy modeling, provides building managers and engineers with a robust analytical framework to optimize maintenance scheduling, refurbishment planning, and resource allocation for sustainable lifecycle management under uncertainty.