Pan, X.; Huang, J.; Zhang, Y.; Zuo, Z.; Zhang, L. Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models. Sensors2024, 24, 1495.
Pan, X.; Huang, J.; Zhang, Y.; Zuo, Z.; Zhang, L. Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models. Sensors 2024, 24, 1495.
Pan, X.; Huang, J.; Zhang, Y.; Zuo, Z.; Zhang, L. Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models. Sensors2024, 24, 1495.
Pan, X.; Huang, J.; Zhang, Y.; Zuo, Z.; Zhang, L. Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models. Sensors 2024, 24, 1495.
Abstract
High-rise building machines (HBMs) play a critical role in the successful construction of super-high skyscrapers, providing essential support and ensuring safety. The HBM’s climbing system relies on a jacking mechanism consisting of several independent jacking cylinders. A reliable control system is imperative to maintain a smooth posture of the construction steel platform (SP) under the action of the jacking mechanism. This research introduces three multivariate time series neural network models—namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN)—to predict the posture of HBM. The models take pressure and stroke measurements from the jacking cylinders as inputs, and their outputs determine the levelness of the SP and the posture of the HBM at various climbing stages. The development and training of these neural networks are based on historical on-site data, with the predictions subjected to thorough comparative analysis. All proposed neural network models exhibit the capability to dynamically predict the posture of the HBM during the climbing process, using data from sensors. Notably, the GRU model shows better predictive performance.
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
