Integrating structural and operational knowledge into multi-state system modeling: Application in urban infrastructures

Abstract

Modern engineering systems, with their increasing complexity driven by technological advancements and growing interdependencies among components, present a challenge to traditional binary-state models. These models, which classify components as either fully operational or failed, are insufficient for capturing the progressive degradation, redundancy mechanisms, and cascading effects observed in real-world systems. Multi-State System (MSS) modeling, which represents intermediate operability states, is a step forward. However, the current literature overlooks a crucial information source: the system’s internal dynamics. These dynamics, which play a crucial role in shaping the system’s behavior, can be leveraged to enhance the learning process in MSS modeling. This study introduces a novel hybrid MSS modeling methodology that incorporates a system’s internal dynamic - such as network topology, redundancy mechanisms, and operational constraints - within an MSS. The methodology is first applied to a Brazilian power system, demonstrating how internal system characteristics influence the state evolution of individual components over time. This evaluation highlights the ability of the model to capture nuanced operational behavior driven by system-level constraints. The methodology is tested on multiple European transmission systems in a second stage to assess its predictive performance in estimating key reliability metrics. The proposed approach consistently outperforms existing models, achieving significantly lower prediction errors by accounting for internal constraints and the system’s dynamics. This work offers a generalizable solution for critical infrastructure planning across domains, enhancing MSS reliability modeling in various engineering systems.