Graph neural networks (GNNs) have attracted much attention in the field of machine learning because of their excellent performance on graph data. Graph data in the architecture, engineering and construction (AEC) sector is very common, such as bubble diagrams for space planning and point clouds for scan-to-BIM models. Some studies in AEC have adopted GNNs to solve practical problems. However, there has been a limited focus on the outcomes of these studies. Therefore, this paper aims to review the applications of GNNs in the building lifecycle. A wide range of existing literature was retrieved. The result shows that the adoption of GNNs is still in its infancy but has been increasing dramatically in recent years. Ten application domains were identified from the planning stage to the operation stage. In addition, the challenges and opportunities of GNNs adoption in AEC were discussed providing directions for future research.

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