[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Shear Strength of Cellular Steel Beams Predicted by Hybrid ANFIS-ECBO Model

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Cellular steel beams, also known as castle beams, are structural members designed to optimize steel structures while maintaining structural integrity. These beams are commonly used in buildings and bridges to support heavy loads and/or long spans. The purpose of this paper is to predict the shear strength of cellular steel beams using artificial intelligence methods using data obtained from experimental results and finite element analysis. The number of data used is 96. The number of inputs is 6 variables, which include beam web thickness, beam flange thickness, web cell height, web cell width, hole distance and number of holes in the beam web, and the output is the cell beam shear strength. To evaluate the model, quality evaluation criteria such as MSE, RMSE, MAE, MAPE, NMAE, NRMSE and correlation coefficient (R) have been used. In this study, the Adaptive Neural Fuzzy Inference System (ANFIS) is improved with ECBO meta-heuristic algorithm and a new hybrid computational model (ANFIS-ECBO) is used to predict the shear strength of cellular steel beams. This paper performs a sensitivity analysis on the prediction of shear strength of cellular steel beams. System identification (SI) is performed using ANFIS to find the most sensitive combinations of input variables. Six different models have been developed based on SI results. The results showed that model 5 gave better answers in both experimental and finite element methods. The highest correlation value and the lowest errors were related to model 5. Model 5 showed that the distance between Holes were the least important input in influencing the results. Also, the number of optimal membership rules has been identified with ANFIS. It was concluded that the ANFIS-ECBO model with 10 membership rules can accurately predict the shear strength of cell beams. Hence, the ANFIS-ECBO-based model can be used as a design tool.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Data availability

Data is provided at the end of the paper.

References

  1. Kaveh, A.; Shokohi, F.: Optimum design of laterally-supported castellated beams using tug of war optimization algorithm. Struct. Eng. Mech. 3(58), 533–553 (2016)

    Article  Google Scholar 

  2. Kaveh, A.; Almasi, P.; Khodagholi, A.: Optimum design of castellated beams using four recently developed meta-heuristic algorithms. Iran. J. Sci. Technol. - Trans. Civ. Eng. 47, 713–725 (2023)

    Article  Google Scholar 

  3. Kaveh, A.; Ghafari, M.H.: Optimum design of steel floor system: effect of floor division number, deck thickness and castellated beams. Struct. Eng. Mech. 59(5), 933–950 (2016). https://doi.org/10.12989/sem.2016.59.5.933

    Article  Google Scholar 

  4. Packer, J.A.; Henderson, J.E.: Hollow Structural Section Connections and Trusses - a Design Guide. The Steel Construction Institute, Ascot (1997)

    Google Scholar 

  5. Kaveh, A.; Fakoor, A.: Cost optimization of steel-concrete composite floor systems with castellated steel beams period. Polytech. Civ. Eng. 65(2), 353–375 (2021). https://doi.org/10.3311/PPci.17184

    Article  Google Scholar 

  6. 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 (2021). https://doi.org/10.1016/j.tws.2021.108592

    Article  Google Scholar 

  7. Limbachiya, V.; Shamass, R.: Application of WORKS 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 

  8. 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 

  9. Moghbeli, A.; Hosseinpour, M.; Sharifi, Y.: Development of neural network models to estimate lateral-distortional buckling resistance of cellular steel beams. Int. J. Optim. Civ. Eng. 12(3), 435–455 (2022)

    Google Scholar 

  10. Ben Seghier, M.E.A.; Carvalho, H.; de Faria, C.C.; Correia, J.A.; Fakury, R.H.: Numerical analysis and prediction of lateral-torsional buckling resistance of cellular steel beams using FEM and least square support vector machine optimized by metaheuristic algorithms. Alexandria Eng. J. 67, 489–502 (2023). https://doi.org/10.1016/j.aej.2022.12.062

    Article  Google Scholar 

  11. Moghbeli, A.; Sharifi, Y.: New predictive equations for lateral-distortional buckling capacity assessment of cellular steel beams. Structures 29, 911–923 (2020). https://doi.org/10.1016/j.istruc.2020.12.004

    Article  Google Scholar 

  12. Ly, H.B.; Le, T.T.: Development of hybrid machine learning models for predicting the critical buckling load of I-shaped cellular beams. Appl. Sci. (2019). https://doi.org/10.3390/app9245458

    Article  Google Scholar 

  13. Nguyen, Q.H.: 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 (Basel) (2020). https://doi.org/10.3390/ma13102210

    Article  Google Scholar 

  14. Abonyi, J.; Andersen, H.; Nagy, L.; Szeifert, F.: Inverse fuzzy-process-model based direct adaptive control. Math. Comput. 51, 119–132 (1999). https://doi.org/10.1016/S0378-4754(99)00142-1

    Article  MathSciNet  Google Scholar 

  15. Benmouiza, K.; Cheknane, A.: Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor. Appl. Climatol. 137(1–2), 31–43 (2019)

    Article  Google Scholar 

  16. Bezdek, J.C.: Cluster validity with fuzzy sets. J. Cybern. 3, 58–73 (1973). https://doi.org/10.1080/01969727308546047

    Article  MathSciNet  Google Scholar 

  17. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, Berlin (1981)

    Book  Google Scholar 

  18. Jafari, M.M.; Ojaghlou, H.; Zare, M.; Schumann, J.G.P.: Application of a novel hybrid wavelet-anfis/fuzzy c-means clustering model to predict groundwater fluctuations. Atmosphere (Basel) 12(1), 1–15 (2021). https://doi.org/10.3390/atmos12010009

    Article  Google Scholar 

  19. Kaveh, A.; Mahdavi, V.R.: Colliding bodies optimization: a novel meta-heuristic method. Comput. Struct. 139, 18–27 (2014). https://doi.org/10.1016/j.compstruc.2014.04.005

    Article  Google Scholar 

  20. Kaveh, A.; Mahdavi, V.R.: Colliding bodies optimization: Extensions and applications. Springer International Publishing, Cham (2015)

    Book  Google Scholar 

  21. Kaveh, A.; Ilchi Ghazaan, M.: Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv. Eng. Softw. 77, 66–75 (2014). https://doi.org/10.1016/j.advengsoft.2014.08.003

    Article  Google Scholar 

  22. Kang, L.; Hong, S.; Liu, X.: Shear behaviour and strength design of cellular beams with circular or elongated openings. Thin-Walled Struct. (2020). https://doi.org/10.1016/j.tws.2020.107353

    Article  Google Scholar 

  23. Kaveh, A.; Khavaninzadeh, N.: Hybrid ECBO – ANN algorithm for shear strength of partially grouted masonry walls. Periodica Polytech. Civ. Eng. 67(4), 1176–1186 (2023). https://doi.org/10.3311/PPci.22653

    Article  Google Scholar 

  24. Chen, J.; Chen, Z.: Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95, 759–771 (2008). https://doi.org/10.1093/biomet/asn034

    Article  MathSciNet  Google Scholar 

  25. Mao, K.Z.: Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Trans. Syst. Man. Cyber Part B 34, 629–634 (2004). https://doi.org/10.1109/TSMCB.2002.804363

    Article  Google Scholar 

  26. Sakamoto, Y.; Ishiguro, M.; Kitagawa, G.: Akaike information criterion statistics. Springer, Cham (1988)

    Google Scholar 

  27. Mallows, C.L.: Some remarks of Cp. Technometrics 42, 87–94 (2012). https://doi.org/10.1080/00401706.2000.10485984

    Article  Google Scholar 

  28. Hou, Z., Shen Q, L.H.: Nonlinear system identification based on ANFIS. In: International Conference on Neural Networks and Signal Processing, 510–512 (2003). https://doi.org/10.1109/ICNNSP.2003.1279323

  29. Shariati, M.; Mafipour, M.S.; Haido, J.H.: Identification of the most influencing parameters on the properties of corroded concrete beams using an adaptive neuro-fuzzy inference system (ANFIS). Steel Compos. Struct. (2020). https://doi.org/10.12989/scs.2020.34.1.000

    Article  Google Scholar 

  30. Khan, M.Z.; Khan, M.F.: Application of ANFIS, ANN and fuzzy time series models to CO2 emission from the energy sector and global temperature increase. Int. J. Clim. Chang. Strateg. Manag. 11(5), 622–642 (2019). https://doi.org/10.1108/IJCCSM-01-2019-0001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering, Iran University of Science and Technology, PO Box, Tehran, 16846-13114, Iran

    Ali Kaveh & Neda Khavaninzadeh

Authors
  1. Ali Kaveh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neda Khavaninzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Kaveh.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest and no financial support being received for this work.

Appendix 1

Appendix 1

See Table 5.

Table 5 Data base used for training the network
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaveh, A., Khavaninzadeh, N. Shear Strength of Cellular Steel Beams Predicted by Hybrid ANFIS-ECBO Model. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09802-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13369-024-09802-z

Keywords

Search

Navigation