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Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

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Abstract

Analytical methods cannot find the exact solution for inelastic lateral torsional buckling. This study aims to develop innovative solutions by creating closed-form equations. A series of numerical studies (ANSYS) have been conducted for buckling load calculation on European I-section cantilever beams reinforced with transverse stiffener plates at different intervals. Formulations were developed using two methods to estimate the found load values more practically. Multiple linear regression analysis and multigene genetic programming methods were used to obtain these formulations. According to the error statistics, the multigene genetic programming method gave more accurate results than the multiple linear regression analysis methods in buckling load estimation. The estimates obtained from the multigene genetic programming method and the numerical results calculated in the ANSYS program were found to be compatible with each other. The scientific novelty brought by the research is to develop more original formulations for cantilever beams instead of using the buckling load calculation described for simply supported beams in the specifications. The scientific difference is that the developed formulations can calculate in a way that can consider the contribution of transverse stiffeners to the buckling load. This study will show that formulations designed with computer technologies can be an alternative calculation method for estimating the lateral buckling load according to the transverse stiffener plate spacing for European I-section cantilever steel beams.

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Abbreviations

a :

The transverse stiffener plate spacing

L :

Cantilever beam length

h :

The depth of cantilever beam section

Aw :

The cross-section area of cantilever beam section

d :

Web height of the beam profile

tw :

Web thickness of the beam profile

C = G.J :

Torsional stiffness

C 1 = E.C w :

Warping stiffness

JD:

The torsion constant of the profile section with a transverse stiffener plate

References

  1. Timoshenko, S.P.; Gere, J.M.: Theory of Elastic Stability. In: Courier Corporation. Courier Corporation (2009)

  2. Demirhan, A.L.; Eroğlu, H.E.; Mutlu, E.O.; Yılmaz, T.; Anil, Ö.: Experimental and numerical evaluation of inelastic lateral-torsional buckling of I-section cantilevers. J. Constr. Steel Res. (2020). https://doi.org/10.1016/j.jcsr.2020.105991

    Article  Google Scholar 

  3. Özbaşaran, H.: Finite differences approach for calculating elastic lateral torsional buckling moment of cantilever I sections. Anadolu Üniversitesi Bilim Ve Teknol. Derg. A-Uygulamalı Bilim. ve Mühendislik. 14, 143–152 (2013)

    Google Scholar 

  4. Vigil, J.: Structural steel design: a practice-oriented approach (2014)

  5. Andrade, A.; Camotim, D.: Lateral-torsional buckling of singly symmetric tapered beams: theory and applications. J. Eng. Mech. 131, 586–597 (2005). https://doi.org/10.1061/(asce)0733-9399(2005)131:6(586)

    Article  Google Scholar 

  6. Dowswell, B.: Lateral-torsional buckling of wide flange cantilever beams. Eng. J. Am. Inst. Steel Constr. 40, 85–91 (2004)

    Google Scholar 

  7. Andrade, A.; Camotim, D.; Dinis, P.B.: Lateral-torsional buckling of singly symmetric web-tapered thin-walled I-beams: 1D model vs. shell FEA. Comput. Struct. 85, 1343–1359 (2007). https://doi.org/10.1016/j.compstruc.2006.08.079

    Article  Google Scholar 

  8. Yuan, W.B.; Kim, B.; Chen, C.Y.: Lateral-torsional buckling of steel web tapered tee-section cantilevers. J. Constr. Steel Res. 87, 31–37 (2013). https://doi.org/10.1016/j.jcsr.2013.03.026

    Article  Google Scholar 

  9. Lu, L.W.; Shen, S.Z.; Shen, Z.Y.; Hu, X.R.: Stability of steel members (1983)

  10. Zhang, L.; Tong, G.S.: Elastic flexural–torsional buckling of thin-walled cantilevers. Thin-Walled Struct. 46, 27–37 (2008). https://doi.org/10.1016/j.tws.2007.08.011

    Article  Google Scholar 

  11. Andrade, A.; Camotim, D.; Providência e Costa, P.: On the evaluation of elastic critical moments in doubly and singly symmetric I-section cantilevers. J. Constr. Steel Res. 63, 894–908 (2007). https://doi.org/10.1016/j.jcsr.2006.08.015

    Article  Google Scholar 

  12. Samanta, A.; Kumar, A.: Distortional buckling in braced-cantilever I-beams. Thin-Walled Struct. 46, 637–645 (2008). https://doi.org/10.1016/j.tws.2007.12.004

    Article  Google Scholar 

  13. Khanh, T.D.; Tuyen, N.M.; Cuong, B.H.: Effects of end-plate on the critical moment of I-section cantilever beam with free end restrained laterally. J. Sci. Technol. Civ. Eng. HUCE 15, 102–109 (2021). https://doi.org/10.31814/STCE.NUCE2021-15(1)-09

    Article  Google Scholar 

  14. Piotrowski, R.; Szychowski, A.: Lateral torsional buckling of steel beams elastically restrained at the support nodes. Appl. Sci. 9(9), 2019 (1944). https://doi.org/10.3390/APP9091944

    Article  Google Scholar 

  15. Piotrowski, R.; Szychowski, A.: Lateral–torsional buckling of beams elastically restrained against warping at supports. Arch. Civ. Eng. 61, 155–174 (2015). https://doi.org/10.1515/ACE-2015-0042

    Article  Google Scholar 

  16. Hassanien, M.; Bahaa, M.; Sobhy, H.; Hassan, A.; Inoue, J.: Effect of vertical web stiffeners on lateral torsional buckling behavior of cantilever steel I-beams. J. Appl. Mech. 7, 233–246 (2004). https://doi.org/10.2208/journalam.7.233

    Article  Google Scholar 

  17. Cường, B.H.: Ảnh hưởng của sườn đầu dầm đến mômen tới hạn của dầm công xôn tiết diện chữ I. Tạp chí Khoa học Công nghệ Xây dựng 13, 20–27 (2019). https://doi.org/10.31814/STCE.NUCE2019-13(5V)-03

    Article  Google Scholar 

  18. Jáger, B.; Dunai, L.: Nonlinear imperfect analysis of corrugated web beams subjected to lateral–torsional buckling. Eng. Struct. (2021). https://doi.org/10.1016/J.ENGSTRUCT.2021.112888

    Article  Google Scholar 

  19. Jáger, B.; Dunai, L.; Kövesdi, B.: Lateral-torsional buckling strength of corrugated web girders: experimental study. Structures 43, 1275–1290 (2022). https://doi.org/10.1016/J.ISTRUC.2022.07.053

    Article  Google Scholar 

  20. Qiao, P.; Zou, G.; Davalos, J.F.: Flexural–torsional buckling of fiber-reinforced plastic composite cantilever I-beams. Compos. Struct. 60, 205–217 (2003). https://doi.org/10.1016/S0263-8223(02)00304-5

    Article  Google Scholar 

  21. Pinarbasi, S.: Lateral torsional buckling of rectangular beams using variational iteration method. Sci. Res. Essays 6, 1445–1457 (2011). https://doi.org/10.5897/SRE11.032

    Article  Google Scholar 

  22. Kalkan, İ.; Ertenli, M.F.; Baş, S.: Petek Kirişlerde Yanal Stabilite Sorunun İncelenmesi ve Karşılaştırmalı Sonuçlar. In: 6. ÇELİK YAPILAR SEMPOZYUMU (2015)

  23. Yilmaz, T.; Kirac, N.: Analytical and parametric investigations on lateral torsional buckling of European IPE and IPN beams. Int. J. Steel Struct. 17, 695–709 (2017). https://doi.org/10.1007/s13296-017-6024-6

    Article  Google Scholar 

  24. Zhang, W.F.; Liu, Y.C.; Hou, G.L.; Chen, K.S.; Ji, J.; Deng, Y.; Deng, S.L.: Lateral-torsional buckling analysis of cantilever beam with tip lateral elastic brace under uniform and concentrated load. Int. J. Steel Struct. 16, 1161–1173 (2016). https://doi.org/10.1007/s13296-016-0052-5

    Article  Google Scholar 

  25. Ozbasaran, H.; Aydin, R.; Dogan, M.: An alternative design procedure for lateral-torsional buckling of cantilever I-beams. Thin-Walled Struct. 90, 235–242 (2015). https://doi.org/10.1016/j.tws.2015.01.021

    Article  Google Scholar 

  26. Gillich, G.R.; Maia, N.M.M.; Wahab, M.A.; Tufisi, C.; Korka, Z.I.; Gillich, N.; Pop, M.V.: Damage detection on a beam with multiple cracks: a simplified method based on relative frequency shifts. Sensors 21, 5215 (2021). https://doi.org/10.3390/S21155215

    Article  Google Scholar 

  27. Sharifi, Y.; Tohidi, S.: Lateral–torsional buckling capacity assessment of web opening steel girders by artificial neural networks: elastic investigation. Front. Struct. Civ. Eng. 8, 167–177 (2014). https://doi.org/10.1007/s11709-014-0236-z

    Article  Google Scholar 

  28. Onchis, D.M.; Gillich, G.R.: Stable and explainable deep learning damage prediction for prismatic cantilever steel beam. Comput. Ind. 125, 103359 (2021). https://doi.org/10.1016/J.COMPIND.2020.103359

    Article  Google Scholar 

  29. Kamane, S.K.; Patil, N.K.; Patagundi, B.R.: Prediction of twisting performance of steel I beam bonded exteriorly with fiber reinforced polymer sheet by using neural network. Mater. Today Proc. 43, 514–519 (2021). https://doi.org/10.1016/J.MATPR.2020.12.026

    Article  Google Scholar 

  30. Nguyen, T.-A.; Ly, H.-B.; Tran, V.Q.: Investigation of ANN architecture for predicting load-carrying capacity of castellated steel beams. Complexity (2021). https://doi.org/10.1155/2021/6697923

    Article  Google Scholar 

  31. Limbachiya, V.; Shamass, R.: Application of artificial neural networks for web-post shear resistance of cellular steel beams. Thin-Walled Struct. (2021). https://doi.org/10.1016/J.TWS.2020.107414

    Article  Google Scholar 

  32. Graciano, C.; Kurtoglu, A.E.; Casanova, E.: Machine learning approach for predicting the patch load resistance of slender austenitic stainless steel girders. Structures 30, 198–205 (2021). https://doi.org/10.1016/J.ISTRUC.2021.01.012

    Article  Google Scholar 

  33. Nguyen, Q.H.; Ly, H.B.; Le, T.T.; Nguyen, T.A.; Phan, V.H.; Tran, V.Q.; Pham, B.T.: Parametric investigation of particle swarm optimization to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. Materials (2020). https://doi.org/10.3390/ma13102210

    Article  Google Scholar 

  34. Hosseinpour, M.; Rossi, A.; Sander Clemente de Souza, A.; Sharifi, Y.: New predictive equations for LDB strength assessment of steel–concrete composite beams. Eng. Struct. (2022). https://doi.org/10.1016/J.ENGSTRUCT.2022.114121

    Article  Google Scholar 

  35. Mohanty, N.; Suvendu, S.K.; Mishra, U.K.; Sahu, S.K.: Experimental and computational analysis of free in-plane vibration of curved beams. J. Vib. Eng. Technol. 1, 3 (2022). https://doi.org/10.1007/s42417-022-00670-1

    Article  Google Scholar 

  36. Neves, M.; Basaglia, C.; Camotim, D.: Stiffening optimisation of conventional cold-formed steel cross-sections based on a multi-objective genetic algorithm and using generalised beam theory. Thin-Walled Struct. (2022). https://doi.org/10.1016/J.TWS.2022.109713

    Article  Google Scholar 

  37. Laman, M.; Uncuoglu, E.: Prediction of the moment capacity of pier foundations in clay using neural networks. Kuwait J. Sci. Eng. 36, 33–52 (2009)

    Google Scholar 

  38. Altun, F.; Dirikgil, T.: The prediction of prismatic beam behaviours with polypropylene fiber addition under high temperature effect through ANN, ANFIS and fuzzy genetic models. Compos. Part B Eng. 52, 362–371 (2013). https://doi.org/10.1016/j.compositesb.2013.04.015

    Article  Google Scholar 

  39. Citakoglu, H.: Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Comput. Electron. Agric. 118, 28–37 (2015). https://doi.org/10.1016/j.compag.2015.08.020

    Article  Google Scholar 

  40. Bayram, S.; Al-Jibouri, S.: Efficacy of estimation methods in forecasting building projects’ costs. J. Constr. Eng. Manag. 142, 05016012 (2016). https://doi.org/10.1061/(asce)co.1943-7862.0001183

    Article  Google Scholar 

  41. Citakoglu, H.: Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theor. Appl. Climatol. 130, 545–556 (2017). https://doi.org/10.1007/s00704-016-1914-7

    Article  Google Scholar 

  42. Limbachiya, V.; Shamass, R.: Application of artificial neural networks for web-post shear resistance of cellular steel beams. Thin-Walled Struct. 161, 107414 (2021). https://doi.org/10.1016/j.tws.2020.107414

    Article  Google Scholar 

  43. Aytek, A.; Kişi, Ö.: A genetic programming approach to suspended sediment modelling. J. Hydrol. 351, 288–298 (2008). https://doi.org/10.1016/j.jhydrol.2007.12.005

    Article  Google Scholar 

  44. Danandeh Mehr, A.; Kahya, E.; Olyaie, E.: Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J. Hydrol. 505, 240–249 (2013). https://doi.org/10.1016/j.jhydrol.2013.10.003

    Article  Google Scholar 

  45. Searson, D.P.; Leahy, D.E.; Willis, M.J.: GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of the International multiconference of engineers and computer scientists (2010)

  46. Kumar, B.; Jha, A.; Deshpande, V.; Sreenivasulu, G.: Regression model for sediment transport problems using multi-gene symbolic genetic programming. Comput. Electron. Agric. 103, 82–90 (2014). https://doi.org/10.1016/j.compag.2014.02.010

    Article  Google Scholar 

  47. Muduli, P.K.; Das, S.K.: CPT-based seismic liquefaction potential evaluation using multi-gene genetic programming approach. Indian Geotech. J. 44, 86–93 (2014). https://doi.org/10.1007/s40098-013-0048-4

    Article  Google Scholar 

  48. Cobaner, M.; Babayigit, B.; Dogan, A.: Estimation of groundwater levels with surface observations via genetic programming. J. Am. Water Works Assoc. 108, E335–E348 (2016). https://doi.org/10.5942/jawwa.2016.108.0078

    Article  Google Scholar 

  49. Citakoglu, H.; Babayigit, B.; Haktanir, N.A.: Solar radiation prediction using multi-gene genetic programming approach. Theor. Appl. Climatol. 142, 885–897 (2020). https://doi.org/10.1007/s00704-020-03356-4

    Article  Google Scholar 

  50. Ferreira, F.P.V.; Shamass, R.; Limbachiya, V.; Tsavdaridis, K.D.; Martins, C.H.: Lateral–torsional buckling resistance prediction model for steel cellular beams generated by artificial neural networks (ANN). Thin-Walled Struct. 170, 108592 (2022). https://doi.org/10.1016/J.TWS.2021.108592

    Article  Google Scholar 

  51. Sharifi, Y.; Moghbeli, A.; Hosseinpour, M.; Sharifi, H.: Neural networks for lateral torsional buckling strength assessment of cellular steel I-beams. Adv. Struct. Eng. 22, 2192–2202 (2019). https://doi.org/10.1177/1369433219836176

    Article  Google Scholar 

  52. Abambres, M.; Rajana, K.; Tsavdaridis, K.D.; Ribeiro, T.P.: Neural network-based formula for the buckling load prediction of I-section cellular steel beams. Computers (2019). https://doi.org/10.3390/COMPUTERS8010002

    Article  Google Scholar 

  53. Hosseinpour, M.; Moghbeli, A.; Sharifi, Y.: Evaluation of lateral-distortional buckling strength of castellated steel beams using regression models. Innov. Infrastruct. Solut. 6, 1–13 (2021). https://doi.org/10.1007/S41062-021-00510-3/FIGURES/10

    Article  Google Scholar 

  54. Moghbeli, A.; Sharifi, Y.: New predictive equations for lateral-distortional buckling capacity assessment of cellular steel beams. Structures 29, 911–923 (2021). https://doi.org/10.1016/J.ISTRUC.2020.12.004

    Article  Google Scholar 

  55. D’Aniello, M.; Güneyisi, E.M.; Landolfo, R.; Mermerdaş, K.: Predictive models of the flexural overstrength factor for steel thin-walled circular hollow section beams. Thin-Walled Struct. 94, 67–78 (2015). https://doi.org/10.1016/J.TWS.2015.03.020

    Article  Google Scholar 

  56. Trahair, N.S.: Steel cantilever strength by inelastic lateral buckling. J. Constr. Steel Res. 66, 993–999 (2010). https://doi.org/10.1016/J.JCSR.2010.02.007

    Article  Google Scholar 

  57. AISC (American Institute of Steel Construction).: Specification for structural steel buildings. Chicago (2010)

  58. Ansys Inc.: mechanical user’s guide (2013)

  59. Alpar, R.: Uygulamalı çok değişkenli istatistiksel yöntemlere giriş-I (1997)

  60. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. Stat. Comput. 4, 87–112 (1994)

    Article  Google Scholar 

  61. Gandomi, A.H.; Alavi, A.H.: A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput. Appl. 21, 171–187 (2012). https://doi.org/10.1007/s00521-011-0734-z

    Article  Google Scholar 

  62. Nash, J.E.; Sutcliffe, J.V.: River flow forecasting through conceptual models part I. A discussion of principles. J. Hydrol. 10, 282–290 (1970). https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  63. Willmott, C.J.; Robeson, S.M.; Matsuura, K.: A refined index of model performance. Int. J. Climatol. 32, 2088–2094 (2012). https://doi.org/10.1002/joc.2419

    Article  Google Scholar 

  64. Gandomi, A.H.; Roke, D.A.: Assessment of artificial neural network and genetic programming as predictive tools. Adv. Eng. Softw. 88, 63–72 (2015). https://doi.org/10.1016/j.advengsoft.2015.05.007

    Article  Google Scholar 

  65. Republic of Turkey Ministry of Environment and Urbanization: (DCCPSS 2016) Regulation on Design, Calculation and Construction Principles of Steel Structures (2016)

  66. Özbayrak, A.: Estimation of design bending moments of RC flat slabs under earthquake effect by ANN analysis. Nigde Omer Halisdemir Univ. J. Eng. Sci. 8, 979–991 (2019). https://doi.org/10.28948/NGUMUH.523939

    Article  Google Scholar 

  67. Citakoglu, H.: Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey. Arab. J. Geosci. (2021). https://doi.org/10.1007/S12517-021-08484-3

    Article  Google Scholar 

  68. Başakın, E.E.; Ekmekcioğlu, Ö.; Çıtakoğlu, H.; Özger, M.: A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Comput. Appl. 34, 783–812 (2022). https://doi.org/10.1007/S00521-021-06424-6

    Article  Google Scholar 

  69. Citakoglu, H.; Demir, V.: Developing numerical equality to regional intensity–duration–frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophys. (2022). https://doi.org/10.1007/S11600-022-00883-8

    Article  Google Scholar 

  70. Uncuoglu, E.; Citakoglu, H.; Latifoglu, L., et al.: Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees. Elsevier, Hoboken (2022)

    Google Scholar 

  71. Citakoglu, H.; Coşkun, Ö.: Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environ. Sci. Pollut. Res. (2022). https://doi.org/10.1007/S11356-022-21083-3

    Article  Google Scholar 

  72. Demir, V.; Citakoglu, H.: Forecasting of solar radiation using different machine learning approaches. Neural Comput. Appl. (2022). https://doi.org/10.1007/S00521-022-07841-X

    Article  Google Scholar 

  73. Demir, V.; Yaseen, Z.M.: Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review. Neural Comput. Appl. (2022). https://doi.org/10.1007/S00521-022-07699-Z

    Article  Google Scholar 

  74. Demir, V.: Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: application of Lake Michigan. Theor. Appl. Climatol. 148, 915–929 (2022). https://doi.org/10.1007/S00704-022-03982-0

    Article  Google Scholar 

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by AÖ, MKA and HÇ. The first draft of the manuscript was written by AÖ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ahmet Özbayrak.

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Özbayrak, A., Ali, M.K. & Çıtakoğlu, H. Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners. Arab J Sci Eng 48, 5347–5370 (2023). https://doi.org/10.1007/s13369-022-07445-6

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