BIM-Based DSS for Building Condition Assessment

This chapter outlines the development and implementation of a conceptual model intended to combine probabilistic Decision Support Systems (DSS) with Building Information Modeling (BIM) for the comprehensive assessment of building conditions. The goal of this integration is to improve the analytical capabilities of facility managers by facilitating systematic evaluations of all asset components to determine their current state, anticipate necessary repairs, and predict potential failures of building elements. At the heart of this integration is the use of Bayesian Network models integrated into the BIM framework. This approach enables a detailed understanding of the complex interactions and dependencies among various building elements and systems. By utilizing probabilistic methods, the model evaluates risks and predicts the lifespan of infrastructure components, thus supporting cost-effective and time-efficient preemptive maintenance strategies. The visualization component of the model uses a color-coded scale within the BIM environment to enhance the interpretability of data and expedite the identification of elements requiring attention. This visual representation plays a pivotal role in transforming abstract probabilistic assessments into actionable insights, accessible even to stakeholders without in-depth technical expertise. The effectiveness of the proposed conceptual model is rigorously tested through a case study using existing software tools, to ensure that the integration of BIM with the probabilistic model for building condition assessment is not only theoretically sound but also practically feasible. The validation process assesses the model's accuracy in simulating real-world conditions and its effectiveness in enhancing maintenance decision-making processes, underscoring the potential of advanced DSS in the field of architectural engineering and construction management.