Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Probabilistic bearing capacity assessment for cross-bracings with semi-rigid connections in transmission towers

  • Zhengqi Tang (School of Civil Engineering, Chongqing University) ;
  • Tao Wang (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Zhengliang Li (School of Civil Engineering, Chongqing University)
  • Received : 2022.04.30
  • Accepted : 2024.02.01
  • Published : 2024.02.10
⟨ Previous Next ⟩

Abstract

In this paper, the effect of semi-rigid connections on the stability bearing capacity of cross-bracings in steel tubular transmission towers is investigated. Herein, a prediction method based on the hybrid model which is a combination of particle swarm optimization (PSO) and backpropagation neural network (BPNN) is proposed to accurately predict the stability bearing capacity of cross-bracings with semi-rigid connections and to efficiently conduct its probabilistic assessment. Firstly, the establishment of the finite element (FE) model of cross-bracings with semi-rigid connections is developed on the basis of the development of the mechanical model. Then, a dataset of 7425 samples generated by the FE model is used to train and test the PSO-BPNN model, and the accuracy of the proposed method is evaluated. Finally, the probabilistic assessment for the stability bearing capacity of cross-bracings with semi-rigid connections is conducted based on the proposed method and the Monte Carlo simulation, in which the geometric and material properties including the outer diameter and thickness of cross-sections and the yield strength of steel are considered as random variables. The results indicate that the proposed method based on the PSO-BPNN model has high accuracy in predicting the stability bearing capacity of cross-bracings with semi-rigid connections. Meanwhile, the semi-rigid connections could enhance the stability bearing capacity of cross-bracings and the reliability of cross-bracings would significantly increase after considering semi-rigid connections.

Keywords

Acknowledgement

This work presented in this paper was fully supported by the NSFC-JSPS China-Japan Scientific Cooperation Project (NSFC Grant No. 51611140123) and the National Natural Science Foundation of China (Grant No. 51478064). The authors would like to express their gratitude for all supports.

References

  1. Abdalla, K.M. and Stavroulakis, G.E. (1995), "A backpropagation neural network model for semi-rigid steel connections", Comput.-Aid. Civil Infrastr. Eng., 10, 77-87. https://doi.org/10.1111/j.1467-8667.1995.tb00271.x.
  2. Anderson, D., Hines, E.L., Arthur, S.J. and Eiap, E.L. (1997), "Application of artificial neural networks to the prediction of minor axis steel connections", Comput. Struct., 63, 685-692. https://doi.org/10.1016/S0045-7949(96)00080-6.
  3. Ang, A.H.S. and Tang, W.H. (2007), Probability Concepts in Engineering, 2nd Edition, John Wiley and Sons, New York, USA.
  4. ASCE 10-15 (2015), Design of Latticed Steel Transmission Structures, American Society of Civil Engineering, Reston, USA.
  5. Bose, N.K. and Liang, P. (1996), Neural Network Fundamentals with Graphs, Algorithms and Applications, McGraw-Hill, New York, USA.
  6. Cannon, D.D. and Nickerson, R. (1993), "Results of TLMRC research on x-bracing strength in lattice transmission towers", IEEE Trans. Power Delivery, 8(1), 285-293. https://doi.org/10.1109/61.180348.
  7. Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A.S., Shi, F. and Le, D.N. (2017), "Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm", Struct. Eng. Mech., 63(4), 429-438. https://doi.org/10.12989/sem.2017.63.4.429.
  8. DL/T 5486-2020 (2020), Technical Specification for the Design of Steel Supporting Structures of Overhead Transmission Line, China Planning Press, Beijing, China.
  9. Elishakoff, I. (1983), Probabilistic Methods in the Theory of Structures, John Wiley and Sons, New York, USA.
  10. Elishakoff, I. (1999), Probabilistic Theory of Structures, Dover Publications, New York, USA.
  11. Gori, M. and Tesi, A. (1992), "On the problem of local minima in backpropagation", IEEE Trans. Patt. Anal. Mach. Intel., 14(1), 76-86. https://doi.org/10.1109/34.107014.
  12. Haykin, S. (1998), Neural Networks a Comprehensive Foundation, Prentice Hall, New Jersey, USA.
  13. Hore, S., Chatterjee, S., Sarkar, S., Dey, N., Ashour, A.S., Balas-Timar, D. and Balas, V.E. (2016), "Neural-based prediction of structural failure of multistoried RC buildings", Struct. Eng. Mech., 58(3), 459-473. http://doi.org/10.12989/sem.2016.58.3.459.
  14. Ikeda, K. and Murota K. (2008), "Asymptotic and probabilistic approach to buckling of structures and materials", Appl. Mech. Rev., 61(4), 040801. https://doi.org/10.1115/1.2939583.
  15. Issa, R.R.A. and Avent, R.R. (1995), "Discrete-field stability analysis of X-braced lattices", J. Struct. Eng., 121(8), 1213-1220. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:8(1213).
  16. Kemp, A.R. and Behncke, R.H. (1998), "Behavior of cross-bracing in latticed towers", J. Struct. Eng., 124(4), 360-367. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:4(360).
  17. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN95-International Conference on Neural Networks, Perth, Australia, November.
  18. Kueh, A.B.H. (2021), "Artificial neural network and regressed beam-column connection explicit mathematical momentrotation expressions", J. Build. Eng., 43, 103195. https://doi.org/10.1016/j.jobe.2021.103195.
  19. Lee, Y., Oh, S.H. and Kim, M.W. (1991), "The effect of initial weights on premature saturation in back-propagation learning", IJCNN-91-Seattle International Joint Conference on Neural Networks, Seattle, USA, July.
  20. Li, H.N., Tang, S.Y. and Yi, T.H. (2013), "Wind-rain-induced vibration test and analytical method of high-voltage transmission tower", Struct. Eng. Mech., 48(4), 435-453. https://doi.org/10.12989/sem.2013.48.4.435.
  21. Mukherjee, A., Deshpande, J.M. and Anmala, J. (1996), "Prediction of buckling load of columns using artificial neural networks", J. Struct. Eng., 122(11), 1385-1387. https://doi.org/10.1061/(ASCE)0733-9445(1996)122:11(1385).
  22. Nguyen, M.S.T. and Kim, S.E. (2021), "A hybrid machine learning approach in prediction and uncertainty quantification of ultimate compressive strength of RCFST columns", Constr. Build. Mater., 302, 124208. https://doi.org/10.1016/j.conbuildmat.2021.124208.
  23. Nguyen, M.S.T., Trinh, M.C. and Kim, S.E. (2021), "Uncertainty quantification of ultimate compressive strength of CCFST columns using hybrid machine learning model", Eng. Comput., 38, 2719-2738. https://doi.org/10.1007/s00366-021-01339-1.
  24. Pan, H.Y., Li, C. and Tian, L. (2020), "Seismic response and failure analyses of pile-supported transmission towers on layered ground", Struct. Eng. Mech., 76(2), 223-237. https://doi.org/10.12989/sem.2020.76.2.223.
  25. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Learning representations by back-propagating errors", Nat., 323, 533-536. https://doi.org/10.1038/323533a0.
  26. Schalkoff, R.J. (1997), Artificial Neural Networks, McGraw-Hill, New York, USA.
  27. Stoman, S.H. (1988), "Stability criteria for x-bracing systems", J. Eng. Mech., 114(8), 1426-1434. https://doi.org/10.1061/(ASCE)0733-9399(1988)114:8(1426).
  28. Stoman, S.H. (1989), "Effective length spectra for cross bracings", J. Eng. Mech., 115(12), 3112-3122. https://doi.org/10.1061/(ASCE)0733-9445(1989)115:12(3112).
  29. Tahir, Z.U.R. and Mandal, P. (2017), "Artificial neural network prediction of buckling load of thin cylindrical shells under axial compression", Eng. Struct., 152, 843-855. https://doi.org/10.1016/j.engstruct.2017.09.016.
  30. Tang, Z.Q., Li, Z.L. and Wang, T. (2023), "Direct prediction method for semi-rigid behavior of K-joint in transmission towers based on surrogate model", Int. J. Struct. Stab. Dyn., 23(3), 2350027. https://doi.org/10.1142/S021945542350027X.
  31. Tapia-Hernandez, E. and De-Leon-Escobedo, D. (2021), "Vulnerability of transmission towers under intense wind loads", Struct. Infrastr. Eng., 18(9), 1235-1250. https://doi.org/10.1080/15732479.2021.1894183.
  32. Thevendran, V. and Wang, C.M. (1993), "Stability of nonsymmetric cross-bracing systems", J. Struct. Eng., 119(1), 169-180. https://doi.org/10.1061/(ASCE)0733-9445(1993)119:1(169).
  33. Tian, L., Pan, H.Y., Ma, R.S. and Dong, X. (2019), "Seismic failure analysis and safety assessment of an extremely long-span transmission tower-line system", Struct. Eng. Mech., 71(3), 305-315. https://doi.org/10.12989/sem.2019.71.3.305.
  34. Zhang, Y., Li, Z.L., Liu, H.J. and Liu, S.Y. (2019), "Study on the stability bearing capacity of unequal angle cross bracing of ultra-high voltage transmission tower", J. South China Univ. Technol., 47(7), 19-31. https://doi.org/10.1061/10.12141/j.issn.1000-565X.180488.