A BIM-Integrated Digital Twin Framework with AI and IoT for Real-Time Earthmoving Fleet Management in Infrastructure Construction

Abstract

Integratingartificial intelligence (AI), the Internet of Things (IoT), and Building Information Modeling (BIM) holds considerable promise for modernizing construction management, yet a unified real-time framework connecting these technologies for heavy civil earthmoving remains lacking. This paper presents BIM-iDT, a BIM-Integrated Digital Twin framework that couples multi-source IoT sensing with an IFC-based BIM model to enable intelligent fleet coordination and automated progress control. The research follows a design-science methodology comprising framework formulation, modular development, field deployment, and multi-project validation. The framework comprises a heterogeneous sensor fusion layer aligning GPS, IMU, fuel-consumption, and LiDAR data within the BIM coordinate system; a spatio-temporal graph attention network (ST-GAT) that recognizes equipment states and predicts short-horizon productivity by modeling fleet-level spatial dependencies; a temporal point cloud differencing module that quantifies cut/fill volumes against BIM design surfaces; and a constrained multi-objective evolutionary optimizer (CMOEO) that generates Pareto-optimal dispatch plans balancing fuel, cycle time, utilization, and schedule adherence. Validation on a highway project with instrumented machines shows that ST-GAT achieves a macro-averaged F1 of 0.943, volume MAPE stays below 3%, and CMOEO reduces fuel consumption by 12.6% and cycle time by 9.3% while maintaining schedule adherence above 96%, yielding an estimated 168-ton CO2 emission reduction. End-to-end latency averages 600 ms, satisfying real-time requirements. Cross-project transfer experiments on a secondary dam construction site further confirm framework generalizability, establishing BIM-iDT as a scalable paradigm for AI-and-IoT-enabled smart construction in infrastructure engineering.