Multi-source information fusion for dynamic safety risk prediction of aerial building machine using spatial–temporal multi-graph convolution network

Aerial Building Machine (ABM) is an innovative and comprehensive construction equipment employed in high-rise building construction, capable of climbing upwards through the use of multiple hydraulic cylinders. The lifting operation of ABM is a critical phase for construction safety, yet there is limited research on forecasting and warning for the ABM lifting process. This study proposes a novel spatial–temporal forecasting model that combines the Graph Neural Network (GNN) and Temporal Convolutional Network (TCN), along with a computational framework for modeling multi-source data fusion and multi-graph construction. Specifically, fast-DTW and direct-LiNGAM are used to capture and analyze the internal relationships within monitoring data from diverse sensors. The proposed Multi-Graph TCN (MGTCN) fuses spatial and temporal features to make multi-step ahead predictions and provides explanations for dynamic safety risks. An ABM case in China is employed to illustrate the feasibility and effectiveness of the proposed framework. The results indicate that: (1) MGTCN exhibits high accuracy in multi-step prediction, with an average R² of 0.917 at 10-step prediction; (2) MGTCN demonstrates strong robustness, maintaining the R² change rate within 3% for every two steps up to the 20-step prediction; (3) The proposed model outperforms others in terms of generalization and stability when compared to models using single-source data or single-graph construction. The contribution of this research lies in transforming large-scale engineering data into graph-based prior knowledge, which is input into a spatial–temporal fusion GNN prediction model, improving accuracy and robustness. The significance of this study is in addressing a gap in ABM risk prediction and advancing time series prediction research in construction engineering. The utilization of intelligent prediction techniques not only ensures the safety management of lifting operations with ABM but also promises to improve the informatization of construction processes for high-rise buildings.