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Author:

Zhang, Ai-Lin (Zhang, Ai-Lin.) | Feng, Huan (Feng, Huan.) | Jiang, Zi-Qin (Jiang, Zi-Qin.) | Liu, Yi (Liu, Yi.)

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EI Scopus SCIE

Abstract:

Large span reticulated shell structures have broad application prospects. However, due to their complex structures and material properties, accurately predicting the performance of large span reticulated shell structures has always been a challenge. Based on machine learning methods, this paper establishes an intelligent prediction model for the performance of large span K6 reticulated shell structures. First, 3240 verified K6 reticulated shell finite element models are established for a series of parameters that affect the ultimate bearing capacity, thereby generating the database required for machine learning algorithms. Secondly, nine machine learning algorithm models are established based on the open source platform Python, and the generated data sets are trained and tested. Finally, the Shap algorithm is used to explain the model prediction results. The results show that the root mean square errors (RMSE) of the artificial neural network, XGBoost, and gradient boosting in predicting ultimate bearing capacity are 1.31, 1.83, and 2.30 respectively, and the R-squared(R²) are all greater than 0.98, indicating that the above three prediction results are very accurate. The Artificial Neural Network model has the best performance and accuracy in predicting the ultimate bearing capacity of large span K6 reticulated shell structures, with a mean absolute percentage error (MAPE) of 0.023, an R2 value of 0.998 %, and 97 % of the sample relative errors between − 10 % and 10 %. time, indicating that it has high prediction accuracy and generalization ability, and can well capture the nonlinear relationship and complex characteristics between the ultimate bearing capacity and input parameters in large span K6 reticulated shell structures. © 2024 Institution of Structural Engineers

Keyword:

Neural networks Learning algorithms Complex networks Forecasting Shells (structures) Mean square error Bearing capacity Errors Machine learning

Author Community:

  • [ 1 ] [Zhang, Ai-Lin]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zhang, Ai-Lin]Beijing Engineering Research Center of High-Rise and Large-Span Prestressed Steel Structure, Beijing; 100124, China
  • [ 3 ] [Feng, Huan]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Jiang, Zi-Qin]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Jiang, Zi-Qin]Beijing Engineering Research Center of High-Rise and Large-Span Prestressed Steel Structure, Beijing; 100124, China
  • [ 6 ] [Liu, Yi]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China

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Source :

Structures

Year: 2024

Volume: 66

4 . 1 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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