• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 55 Issue 2
Mar.  2020
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Article Contents
QIU Dongwei, WANG Tong, DUAN Mingxu, LUO Dean, WANG Laiyang. Construct and Application of LM-CDBN Deformation Prediction Model for Supertall Buildings[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 310-316. doi: 10.3969/j.issn.0258-2724.20180293
Citation: QIU Dongwei, WANG Tong, DUAN Mingxu, LUO Dean, WANG Laiyang. Construct and Application of LM-CDBN Deformation Prediction Model for Supertall Buildings[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 310-316. doi: 10.3969/j.issn.0258-2724.20180293

Construct and Application of LM-CDBN Deformation Prediction Model for Supertall Buildings

doi: 10.3969/j.issn.0258-2724.20180293
  • Received Date: 15 May 2018
  • Rev Recd Date: 06 Jun 2018
  • Available Online: 08 Jul 2018
  • Publish Date: 01 Apr 2020
  • In order to improve the prediction accuracy of supertall building deformation, the method of adjusting the weight and threshold in the conditional deep belief network (CDBN) model was improved. The LM (Levenberg-Marquardt) algorithm was used as a weighting method to construct the LM-CDBN network model. This method was applied to the deformation prediction of a 298 m supertall building. Then, the model was fully evaluated in terms of training error, goodness of fit, and prediction stability. Finally, the prediction results of LM-CDBN model, deep belief network (DBN) model, extreme learning machine (ELM) and unscented Kalman filter-support vector regression (UKF-SVR) were compared. The result shows that the prediction performance of LM-CDBN was 32%, 55% and 24% higher than three other models respectively. LM-CDBN model improves in the information extraction stability and generalization ability of solving nonlinear problems in time-varying systems.

     

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