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Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models.

Xi Pan1, Junguang Huang1,2, Yiming Zhang2

  • 1General Engineering Institute of Shanghai Construction Group, Shanghai Construction Group Co., Ltd., Shanghai 200080, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

This study uses Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks to predict high-rise building machine (HBM) posture. GRU models show stronger robustness in ensuring stable skyscraper construction.

Keywords:
high-rise building machine (HBM)multivariate time series (MTS)neural networks (NNs)posture predictionsteel platform (SP)

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Area of Science:

  • Construction Engineering and Management
  • Artificial Intelligence in Civil Engineering

Background:

  • High-rise building machines (HBMs) are crucial for super-high skyscraper construction, requiring precise control of their climbing systems.
  • The jacking mechanism, composed of independent jacking cylinders, necessitates a reliable control system to maintain the steel platform's (SP) posture.
  • Accurate prediction of HBM posture is essential for ensuring safety and stability during construction.

Purpose of the Study:

  • To evaluate the effectiveness of multivariate time series (MTS) neural network models for predicting HBM posture.
  • To compare the predictive performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN) models.
  • To identify the key parameters influencing HBM posture control for improved work stability.

Main Methods:

  • Developed and trained LSTM, GRU, and TCN models using historical on-site data from HBM operations.
  • Utilized jacking cylinder pressure and stroke measurements as input variables for the MTS models.
  • Analyzed model predictions for SP levelness and HBM posture, including comparative analysis and sensitivity analysis.

Main Results:

  • LSTM and GRU models demonstrated comparable performance in predicting HBM posture, with median R² values of 0.903 and 0.871, respectively.
  • The GRU model exhibited superior robustness, indicated by a lower median Mean Absolute Error (MAE) of 0.4.
  • Sensitivity analysis revealed that jacking cylinder stroke and pressure significantly impact SP levelness and HBM posture.

Conclusions:

  • MTS neural network-based prediction models are effective for controlling HBM posture and enhancing work stability.
  • Adjusting jacking cylinder pressure is a viable method for real-time HBM posture correction.
  • The findings provide valuable insights for developing advanced control systems for high-rise building machinery.