A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction

In the construction of super high-rise buildings, vibration monitoring of climbing scaffolding is crucial for ensuring construction safety. This study proposes a vibration state prediction model based on Variational Mode Decomposition (VMD) and Temporal Convolutional Network (TCN), referred to as the VMD-TCN model. Using the construction of the Tianjin Zhonghai City Plaza super high-rise building as a case study, this model was applied to 48 h of climbing scaffolding vibration data for modeling and prediction. The results demonstrate that VMD significantly enhances the multi-band feature extraction capability of vibration signals. Compared to predictions using raw, undecomposed signals, the VMD-TCN model reduces the root mean square error (RMSE) by 43.9%, 43.2%, and 34.7% for 1 min, 3 min, and 5 min prediction tasks, respectively, while improving the coefficient of determination (R2) by 21.0%, 33.0%, and 37.6%. Furthermore, the computational efficiency of the VMD-TCN model surpasses that of the VMD-GRU model by approximately 88–91%, making it well-suited for engineering applications with high real-time requirements. Additionally, the VMD-TCN model maintains high predictive accuracy across different sensor placements and data collection periods, demonstrating strong generalization capabilities. The findings of this study provide scientific support for intelligent monitoring and safety early warning of climbing scaffolding, contributing to improved safety and management efficiency in super high-rise building construction.