Skip to main content
Log in

Artificial-Neural-Network-Based Mechanical Simulation Prediction Method for Wheel-Spoke Cable Truss Construction

  • Published:
International Journal of Steel Structures Aims and scope Submit manuscript

Abstract

Cable force monitoring is an important step in cable truss structural health monitoring. Considering cost effectiveness, the accuracy and quality of safety assessments depend primarily on the usage of reasonable cable monitoring programs. Many monitoring methods have been proposed to design the cable truss structure. The emergence of artificial neural network (ANN) models has resulted in improved predictive abilities. In this study, an ANN-based model is used to estimate parameter changes in static and prestress loss tests during the construction of a cable truss. The finite element model data of 243 cases are analysed by ANSYS and MATLAB. Analysis results indicate the excellent prediction performance as well as high accuracy and generalization of the proposed ANN-based model. Furthermore, the successfully trained ANN-based model is used to predict new cases. As an alternative to finite element analysis and physical test, the proposed model can guide the static loading of the spoke cable truss structure and thus allow the safe usage of the structure during service.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  • Bakhary, N., Hao, H., & Deeks, A. J. (2007). Damage detection using artificial neural network with consideration of uncertainties. Engineering Structures, 29(11), 2806–2815

    Article  Google Scholar 

  • Beale M, Hagan M, Demuth H. MATLAB Deep Learning Toolbox™ User’s Guide: PDF Documentation for Release R2019a. The MathWorks Inc, 2019.

  • Belenja E I. Prestressed load-bearing metal structures. Mir, 1977.

  • Chen, M. (2013). Principles and examples of MATLAB neural network [M]. (pp. 4–165). Tsinghua University Press.

    Google Scholar 

  • Chen, Z. H., Wu, Y. J., Yin, Y., et al. (2010). Formulation and application of multi-node sliding cable element for the analysis of Suspen-Dome structures. Finite Elements in Analysis and Design, 46(9), 743–750

    Article  MathSciNet  Google Scholar 

  • Gomes, H. M., Awruch, A. M., & Lopes, P. A. M. (2011). Reliability based optimization of laminated composite structures using genetic algorithms and Artificial Neural Networks. Structural Safety, 33(3), 186–195

    Article  Google Scholar 

  • Hashemi, S. S., Sadeghi, K., Fazeli, A., et al. (2019). Predicting the Weight of the Steel Moment-Resisting Frame Structures Using Artificial Neural Networks. International Journal of Steel Structures, 19(1), 168–180

    Article  Google Scholar 

  • Khoa, N. L. D., Alamdari, M. M., Rakotoarivelo, T., et al. (2018). Structural health monitoring using machine learning techniques and domain knowledge based features[M]//Human and Machine Learning. (pp. 409–435). Springer.

    Google Scholar 

  • Kim, B. H., & Park, T. (2007). Estimation of cable tension force using the frequency-based system identification method. Journal of Sound and Vibration, 304(3–5), 660–676

    Article  Google Scholar 

  • Krishnan, S. (2020). Structural design and behavior of prestressed cable domes. Engineering Structures. https://doi.org/10.1016/j.engstruct.2020.110294

    Article  Google Scholar 

  • Kurian B, Liyanapathirana R. (2020) Machine Learning Techniques for Structural Health Monitoring[C]//Proceedings of the 13th International Conference on Damage Assessment of Structures. Springer, Singapore, pp. 3–24.

  • Li, S., Wei, S., Bao, Y., et al. (2018). Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio. Engineering Structures, 155, 1–15

    Article  Google Scholar 

  • Liu, X., Zhan, X., Zhang, A., et al. (2017). Random imperfection method for stability analysis of a suspended dome[J]. International Journal of Steel Structures, 17(1), 91–103

    Article  Google Scholar 

  • Liu, Z. S., Han, Z. B., He, J., et al. (2018). Sensitivity test and reliability evaluation of cable relaxation of spoke cable truss. Journal of Tongji University (natural Science Edition), 47, 07 (in Chinese).

    Google Scholar 

  • Liu, Z. S., Wang, J. C., Han, Z. B., et al. (2019). Sensitivity test and reliability evaluation of length error of cable truss with spokes. Journal of Tianjin University (natural Science and Engineering Technology Edition), 52(S2), 23–30 (in Chinese).

    Google Scholar 

  • Magnel, G. (1950). Prestressed steel structures. The Structural Engineer, 28(11), 285–295

    Google Scholar 

  • Mehrabi, A. B., & Tabatabai, H. (1998). Unified finite difference formulation for free vibration of cables. Journal of Structural Engineering, 124(11), 1313–1322

    Article  Google Scholar 

  • Rizzo, F., & Caracoglia, L. (2020). Artificial Neural Network model to predict the flutter velocity of suspension bridges. Computers and Structures, 233, 106236

    Article  Google Scholar 

  • Roy, K., Lau, H. H., Ting, T. C. H., et al. (2020). Flexural capacity of gapped built-up cold-formed steel channel sections including web stiffeners. Journal of Constructional Steel Research, 172, 106154

    Article  Google Scholar 

  • Roy, K., Lau, H. H., Ting, T. C. H., et al. (2021). Flexural behaviour of back-to-back built-up cold-formed steel channel beams: Experiments and finite element modelling[C]//Structures. Elsevier, 29, 235–253

    Google Scholar 

  • Roy, K., Mohammadjani, C., & Lim, J. B. P. (2019b). Experimental and numerical investigation into the behaviour of face-to-face built-up cold-formed steel channel sections under compression. Thin-Walled Structures, 134, 291–309

    Article  Google Scholar 

  • Roy, K., Ting, T. C. H., Lau, H. H., et al. (2018a). Nonlinear behavior of axially loaded back-to-back built-up cold-formed steel un-lipped channel sections. Steel and Composite Structures, 28(2), 233–250

    Google Scholar 

  • Roy, K., Ting, T. C. H., Lau, H. H., et al. (2018b). Nonlinear behaviour of back-to-back gapped built-up cold-formed steel channel sections under compression. Journal of Constructional Steel Research, 147, 257–276

    Article  Google Scholar 

  • Roy, K., Ting, T. C. H., Lau, H. H., et al. (2018c). Effect of thickness on the behaviour of axially loaded back-to-back cold-formed steel built-up channel sections - Experimental and numerical investigation. Structure. https://doi.org/10.1016/j.istruc.2018.09.009

    Article  Google Scholar 

  • Roy, K., Ting, T. C. H., Lau, H. H., et al. (2019). Experimental and numerical investigations on the axial capacity of cold-formed steel built-up box sections. Journal of Constructional Steel Research, 160, 411–427

    Article  Google Scholar 

  • Russell, J. C., & Lardner, T. J. (1998). Experimental determination of frequencies and tension for elastic cables. Journal of Engineering Mechanics, 124(10), 1067–1072

    Article  Google Scholar 

  • Sychterz, A. C., & Smith, I. F. C. (2018). Using dynamic measurements to detect and locate ruptured cables on a tensegrity structure. Engineering Structures, 173, 631–642. https://doi.org/10.1016/j.engstruct.2018.06.083

    Article  Google Scholar 

  • Ting, T. C. H., Roy, K., Lau, H. H., et al. (2018). Effect of screw spacing on behavior of axially loaded back-to-back cold-formed steel built-up channel sections. Advances in Structural Engineering, 21(3), 474–487

    Article  Google Scholar 

  • Wu, X., Ghaboussi, J., & Garrett, J. H., Jr. (1992). Use of neural networks in detection of structural damage. Computers and Structures, 42(4), 649–659

    Article  Google Scholar 

  • Zarbaf, S. E. H. A. M., Norouzi, M., Allemang, R., et al. (2018). Vibration-based cable condition assessment: A novel application of neural networks. Engineering Structures, 177, 291–305

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Beijing Natural Science Foundation (No. 8202001). The support is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhansheng Liu.

Ethics declarations

Conflict of interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Jiang, A., Shao, W. et al. Artificial-Neural-Network-Based Mechanical Simulation Prediction Method for Wheel-Spoke Cable Truss Construction. Int J Steel Struct 21, 1032–1052 (2021). https://doi.org/10.1007/s13296-021-00488-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13296-021-00488-9

Keywords

Navigation