Hybrid Physics-Informed Graph Neural Network Surrogate for Multi-Physics Optimization of Net-Zero Prefabricated Modular Buildings Driven by BIM Semantic Data
Masood Karamoozian1, Zarrin Mahdavipour2, Mohammed Ameen3, Faisal Binzagr4, Abdolraheem Khader2,*, Ahmed Hamza Osman3, Ali Ahmed4
1 School of Architecture, Southeast University, Nanjing, China
2 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
3 Department of Information Systems, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University Rabigh, Jeddah, Saudi Arabia
4 Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University Rabigh, Jeddah, Saudi Arabia
* Corresponding Author: Abdolraheem Khader. Email: 
Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.085421
Received 11 May 2026; Accepted 02 July 2026; Published online 28 July 2026
Abstract
Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded as nodes and typed edges in a heterogeneous directed graph, processed by a multi-layer graph attention network (GAT) backbone augmented with physics-informed loss terms. These constraints explicitly enforce energy and mass conservation, transient heat transfer, structural equilibrium, and carbon balance, ensuring physically plausible predictions even under limited training data. Trained on a dataset of more than 852 BIM-derived parametric models coupled with high-fidelity multi-physics simulations (EnergyPlus, OpenSeesPy FEM, One Click LCA), the surrogate achieves mean absolute percentage errors (MAPE) below 7% across energy demand, embodied carbon, operational carbon, and peak structural stress, while delivering inference speedups of up to 1200
× over conventional simulation workflows. Integrated with NSGA-II multi-objective evolutionary optimization, the framework identifies Pareto-optimal designs that reduce operational energy by up to 52% and embodied carbon by 28%–45% relative to baseline configurations. Ablation studies confirm that physics-informed regularization reduces prediction errors by 40%–55% in low-data regimes. The proposed PI-GNN offers a generalizable, interpretable, and physics-consistent surrogate modeling paradigm, bridging BIM semantics with deep learning to accelerate sustainable design and support the global transition toward net-zero prefabricated buildings.
Keywords
Building information modeling; physics-informed neural networks; graph neural networks; prefabricated modular buildings; net-zero buildings; multi-physics optimization; surrogate modeling; NSGA-II; sustainable design
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