AI-Driven Semantic Framework for Automated Construction Planning and Scheduling with BIM and Digital Twin Integration

Construction planning and scheduling, including task sequences, constraints, and interdependencies, is poorly structured within digital models such as BIM and Digital Twin and lacks effective integration with planning documents to support scheduling analysis, logic-based reasoning, and automation. To address this gap, this paper develops an AI-enabled framework organized into three core dimensions: (1) enriching BIM and integrating reality data with activity, spatial, and resource attributes; (2) formalizing planning logic using a planning ontology to represent execution relationships; and (3) applying AI techniques to extract planning knowledge, validate constraints, and generate automated schedules. The framework supports logic-based planning, progress tracking, and coordination across construction processes.