Graph Neural Networks for building and civil infrastructure operation and maintenance enhancement

[Display omitted] •Graph Neural Networks (GNN) in building and civil infrastructure operation and management (OM) data and process enhancement.•Domain-specific feature engineering, architecture and hyperparameter optimisations.•Integration of explainable Artificial Intelligence (AI) techniques.•Future implementation aspects for building and civil infrastructure OM. This systematic review, conducted within the PRISMA framework, investigates the disruptive capabilities of Graph Neural Networks (GNNs) in optimising Operations and Maintenance (OM) practices within the building and civil infrastructure domain. Addressing 5 research questions and encompassing 111 studies from 2014 to 2024, our study identifies the multifaceted applications of GNNs across different project stages from data enhancement to operational scenario enhancement. When considering integrated Facilities Management (FM) approaches, GNNs are employed for data enhancement purposes, leveraging techniques such as semantic enrichment of Building Information Modelling (BIM), various data imputation scenarios, and semantic segmentation of point clouds to enhance data quality and completeness. Operational scenarios involve the utilisation of GNN algorithms for anomaly detection, fault classification, system optimisation, and forecasting. Methodological optimisations crucial for GNN feasibility include feature engineering, architecture optimisation to balance complexity and overfitting risk, and the integration of Explainable Artificial Intelligence (XAI) methods to enhance model validity and trust. Physical principles integration through Physics-Informed Graph Neural Networks (PIGNNs) further enhances model explainability and validation. Future research directions focus on data interoperability enhancement, scalability improvements, and explainability enhancements. Automated graph generation and labelling, heterogeneous GNN models, supporting algorithms such as Long Short-Term Memory (LSTM) and reinforcement learning are proposed to overcome analysis limitations. Specific workflows targeting building performance-based semantic enrichment, building systems data imputation, and interdependency prediction are proposed in future directions. The review highlights the symbiotic relationship between GNN-based analysis and digital twin data analysis, emphasising the suitability of GNNs in addressing the demands of digital twin data analysis in the building and civil infrastructure domain.