Automatic Detection of Construction Conflicts and Risk Prediction Based on the Integration of Building Information Modeling and Graph Neural Networks

The increasing complexity of civil infrastructure projects necessitates advanced methodologies for efficient design, construction, and management. Traditional Building Information Modeling systems, while instrumental in digitizing construction processes, often fall short in addressing dynamic project requirements and complex interdependencies among building components. Conventional approaches primarily rely on rule-based systems and manual interventions, leading to inefficiencies in conflict detection and risk assessment. To overcome these limitations, we propose a framework that integrates BIM with GNNs and demonstrates improved conflict detection and risk prediction accuracy across four benchmark datasets. Our approach conceptualizes BIM data as a high-dimensional graph, where nodes represent building elements and edges denote spatial and functional relationships. We introduce the Structured Multi-Semantic Encoder Network, which captures the intricate interplay between geometry, taxonomy, and compliance features through a variational perspective, effectively handling incomplete or heterogeneous BIM data. We develop the Temporal-Aware Semantic Coordination Engine, an adaptive module that aligns contractor interpretations with standardized ontologies, facilitating dynamic project evolution management. Our framework addresses the critical need for automation and artificial intelligence in construction, aligning with the objectives of enhancing planning, design, and management of civil infrastructure assets. Experimental results demonstrate achieves top-1 performance on all datasets in conflict detection accuracy and risk prediction capabilities, underscoring the potential of our integrated BIM-GNN approach in revolutionizing construction management practices.