Structural Engineering and Mechanics

  • 제79권4호
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  • Pages.499-506
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  • 2021
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An optimized machine learning based moment-rotation analysis of steel pallet rack connections

  • Shah, S.N.R. (Civil Engineering Department, Mehran University of Engineering & Technology, SZAB) ;
  • Hussain, Syed Fawad (Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)
  • 투고 : 2021.04.29
  • 심사 : 2021.07.14
  • 발행 : 2021.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

The moment-rotation (M-θ) response of steel pallet rack (SPR) beam-to-column connections (BCCs) is naturally complex and predicted through repeated experimental investigations motivated by the variety in the geometry of commercially available beam end connectors (BECs). Past literature has shown that even finite element modeling (FEM) was somehow unable to fully capture the structural behavior of SPR BCCs in the plastic region. This study proposes an innovative Support vector machine (SVM)-Discrete Wavelet transform (DWT)-based optimized model for M-θ analysis of SPR BCCs. A data set of total thirty-two experiments on SPR BCCs was used to develop the model. The experimental investigations identified the most influential parameters affecting the M-θ response of SPR BCCs which are column thickness, beam depth, and depth of the BEC. Those parameters were optimized using Firefly algorithm and selected as input parameters. Reliability assessment of proposed predictive model was performed using root-mean-square error (RMSE), Pearson coefficient (r), and coefficient of determination 'r-square' (R2). The findings of predictive model were juxtaposed with experimental outcomes and FEM results, available in the literature, and a close agreement was achieved. The R2 value of 0.958 and 0.984 were achieved for moment and rotation predictions, respectively. Hence, the proposed SVM-DWT model can be efficiently used to forecast the optimum and reliable M-θ response of SPR BCCs and to minimize the need of repetitive testing.

키워드

참고문헌

  1. Bernuzzi, C. and Maxenti, F. (2015), "European alternatives to design perforated thin-walled cold-formed beam-columns for steel storage systems", J. Constr. Steel Res., 110, 121-136. https://doi.org/10.1016/j.jcsr.2015.02.021.
  2. Chen, C., Shi, L., Shariati, M., Toghroli, A., Mohamad, E.T., Bui, D.T. and Khorami, M. (2019), "Behavior of steel storage pallet racking connection-A review", Steel Compos. Struct., 30(5), 457. https://doi.org/10.12989/scs.2019.30.5.457.
  3. Cortes, C. and Vapnik, V. (1995), "Support-vector networks 20(3), 273-297 (1995)", Mach. Learn., 20(3), 273-297. https://doi.org/10.1023A:1022627411411. https://doi.org/10.1007/BF00994018
  4. EN-1993 (2005), Eurocode 3: Design of Steel Structures-Part 1-8: Design of Joints, British Standard Institute, London.
  5. Hussain, S.F. (2019), "A novel robust kernel for classifying highdimensional data using Support Vector Machines", Exp. Syst. Appl., 131, 116-131. https://doi.org/10.1016/j.eswa.2019.04.037.
  6. Kalteh, A.M. (2013), "Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform", Comput. Geosci., 54, 1-8. https://doi.org/10.1016/j.cageo.2012.11.015.
  7. Kurtoglu, A.E. (2018), "Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines", Steel Compos. Struct., 29(3), 309-318. https://doi.org/10.12989/scs.2018.29.3.309.
  8. Markazi, F., Beale, R. and Godley, M. (2001), "Numerical modelling of semi-rigid boltless connectors", Comput. Struct., 79(26-28), 2391-2402. https://doi.org/10.1016/S0045-7949(01)00058-X.
  9. Mishra, A., Kumar, B. and Dutta, J. (2016), "Prediction of hydraulic conductivity of soil bentonite mixture using HybridANN approach", J. Environ. Inform., 27(2), 98-105. https://doi.org/10.3808/jei.201500292.
  10. Montuori, R., Gabbianelli, G., Nastri, E. and Simoncelli, M. (2019), "Rigid plastic analysis for the seismic performance evaluation of steel storage racks", Steel Compos. Struct., 32(1), 1-19. https://doi.org/10.12989/scs.2019.32.1.001.
  11. Najafzadeh, M., Etemad-Shahidi, A. and Lim, S.Y. (2016), "Scour prediction in long contractions using ANFIS and SVM", Ocean Eng., 111, 128-135. https://doi.org/10.1016/j.oceaneng.2015.10.053.
  12. Najafzadeh, M. and Oliveto, G. (2020), "Riprap incipient motion for overtopping flows with machine learning models", J. Hydroinform., 22(4), 749-767. https://doi.org/10.2166/hydro.2020.129.
  13. Prabha, P., Marimuthu, V., Saravanan, M. and Jayachandran, S.A. (2010), "Evaluation of connection flexibility in cold formed steel racks", J. Constr. Steel Res., 66(7), 863-872. https://doi.org/10.1016/j.jcsr.2010.01.019.
  14. Qasem, S.N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S. and Chau, K.W. (2019), "Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates", Eng. Appl. Comput. Fluid Mech., 13(1), 177-187. https://doi.org/10.1080/19942060.2018.1564702.
  15. Qu, J., Zhang, Z., Luo, X., Li, B. and Wen, J. (2016), "A novel method to aging state recognition of viscoelastic sandwich structures", Steel Compos. Struct., 21(6), 1183-1210. https://doi.org/10.12989/scs.2016.21.6.1183.
  16. Samui, P. and Kumar, B. (2006), "Artificial neural network prediction of stability numbers for two-layered slopes with associated flow rule", Elec. J. Geotech. Eng., 11, 1-44. https://doi.org/10.1.1.504.395.
  17. Sangle, K.K., Bajoria, K.M. and Talicotti, R.S. (2012), "Elastic stability analysis of cold-formed pallet rack structures with semi-rigid connections", J. Constr. Steel Res., 71, 245-262. https://doi.org/10.1016/j.jcsr.2011.11.002.
  18. Shah, S., Ramli Sulong, N., Khan, R. and Jumaat, M. (2017), "Structural performance of boltless beam end connectors", Adv. Steel Constr., 13(2), 144-159. https://doi.org/10.18057/IJASC.2017.13.2.4.
  19. Shah, S., Sulong, N.R. and El-Shafie, A. (2018), "New approach for developing soft computational prediction models for moment and rotation of boltless steel connections", Thin Wall. Struct., 133, 206-215. https://doi.org/10.1016/j.tws.2018.09.032.
  20. Shah, S., Sulong, N.R., Jumaat, M. and Shariati, M. (2016), "State-of-the-art review on the design and performance of steel pallet rack connections", Eng. Fail. Anal., 66, 240-258. https://doi.org/10.1016/j.engfailanal.2016.04.017.
  21. Shah, S., Sulong, N.R., Khan, R., Jumaat, M. and Shariati, M. (2016), "Behavior of industrial steel rack connections", Mech. Syst. Signal Pr., 70, 725-740. https://doi.org/10.1016/j.ymssp.2015.08.026.
  22. Shah, S., Sulong, N.R., Shariati, M. and Jumaat, M. (2015), "Steel rack connections: identification of most influential factors and a comparison of stiffness design methods", PloS one, 10(10), e0139422. https://doi.org/10.1371/journal.pone.0139422.
  23. Shah, S., Sulong, N.R., Shariati, M., Khan, R. and Jumaat, M. (2016), "Behavior of steel pallet rack beam-to-column connections at elevated temperatures", Thin Wall. Struct., 106, 471-483. https://doi.org/10.1016/j.tws.2016.05.021.
  24. Shariati, M., Tahir, M., Wee, T.C., Shah, S., Jalali, A. and Khorami, M. (2018), "Experimental investigations on monotonic and cyclic behavior of steel pallet rack connections", Eng. Fail. Anal., 85, 149-166. https://doi.org/10.1016/j.engfailanal.2017.08.014.
  25. Shariati, M., Trung, N.T., Wakil, K., Mehrabi, P., Safa, M. and Khorami, M. (2019), "Estimation of moment and rotation of steel rack connections using extreme learning machine", Steel Compos. Struct., 31(5), 427-435. https://doi.org/10.12989/scs.2019.31.5.427.
  26. Thirumalaiselvi, A., Verma, M., Anandavalli, N. and Rajasankar, J. (2018), "Response prediction of laced steel-concrete composite beams using machine learning algorithms", Struct. Eng. Mech., 66(3), 399-409. http://doi.org/10.12989/sem.2018.66.3.399.
  27. Trung, N.T., Shahgoli, A.F., Zandi, Y., Shariati, M., Wakil, K., Safa, M. and Khorami, M. (2019), "Moment-rotation prediction of precast beam-to-column connections using extreme learning machine", Struct. Eng. Mech., 70(5), 639-647. http://doi.org/10.12989/sem.2019.70.5.639
  28. Yun, Y.M., Kang, M.M. and Lee, M.S. (2006), "Optimum design of plane steel frames with PR-connections using refined plastic hinge analysis and genetic algorithm", Struct. Eng. Mech., 23(4), 387-407. http://doi.org/10.12989/sem.2006.23.4.387
  29. Zhao, X., Dai, L., Wang, T., Sivakumaran, K.S. and Chen, Y. (2017), "A theoretical model for the rotational stiffness of storage rack beam-to-upright connections", J. Constr. Steel Res., 133, 269-281. https://doi.org/10.1016/j.jcsr.2017.02.014.