Abstract
Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.
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14 January 2020
The Editor-in-Chief has retracted this article (Toghroli et al. 2018) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited Cojbasic et al. 2016; Mazinani et al. 2016; Mohammadian et al. 2016; Mansourvar et al. 2015) and authorship manipulation. Meldi Suhatril, Zainah Ibrahim, Maryam Safa, Mahdi Shariati and Shahaboddin Shamshirband do not agree to this retraction. Ali Toghroli has not responded to any correspondence about this retraction.
References
Annema, A. J., Hoen, K., & Wallinga, H. (1994). Precision requirements for single-layer feedforward neural networks, In Fourth international conference on microelectronics for neural networks and fuzzy systems (pp. 145–151).
Bilgehan, M. (2011). Comparison of ANFIS and NN models—with a study in critical buckling load estimation. Applied Soft Computing, 11, 3779–3791.
Ghouti, L., Sheltami, T. R., & Alutaibi, K. S. (2013). Mobility prediction in mobile ad hoc networks using extreme learning machines. Procedia Computer Science, 19, 305–312.
Hakim, S. J. S., Noorzaei, J., Jaafar, M., Jameel, M., & Mohammadhassani, M. (2011). Application of artificial neural networks to predict compressive strength of high strength concrete. International Journal of Physical Sciences, 6, 975–981.
Huang, G. B., Chen, L., & Siew, C. K. (2006a). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17, 879–892.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006b). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2003). Real-time learning capability of neural networks, Technical Report ICIS/45/2003. Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. International Joint Conference on Neural Networks, 2, 985–990.
Lali, P., & Setayeshi, S. (2011). A novel approach to develop the control of Telbot using ANFIS for nuclear hotcells. Annals of Nuclear Energy, 38, 2156–2162.
Liang, N. Y., Huang, G. B., Rong, H. J., Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17, 1411–1423.
Maleki, S., & Bagheri, S. (2008). Behavior of channel shear connectors, part I: Experimental study. Journal of Constructional Steel Research, 64, 1333–1340.
Maleki, S., & Mahoutian, M. (2009). Experimental and analytical study on channel shear connectors in fiber-reinforced concrete. Journal of Constructional Steel Research, 65, 1787–1793.
Nian, R., He, B., Zheng, B., Heeswijk, M. V., Yu, Q., Miche, Y., et al. (2014). Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing, 128, 273–284.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. doi:10.1037/h0042519.
Shariati, M., Ramli Sulong, N. H., & Arabnejad Khanouki, M. M. (2010). Experimental and analytical study on channel shear connectors in light weight aggregate concrete. in Proceedings of the 4th international conference on steel & composite structures, 21–23 July, 2010, Sydney, Australia, 2010. Research Publishing Services, doi:10.3850/978-981-08-6218-3.
Shariati, A., Ramli Slong, N. H., Suhatril, M., & Shariati, M. (2012a). Investigation of channel shear connectors for composite concrete and steel T-beam. International Journal of Physical Sciences, 7, 1828–1831.
Shariati, M., Ramli Sulong, N., Suhatril, M., Shariati, A., Arabnejad Khanouki, M., & Sinaei, H. (2012b). Fatigue energy dissipation and failure analysis of channel shear connector embedded in the lightweight aggregate concrete in composite bridge girders. Fifth international conference on engineering failure analysis 1–4 (July 2012). Hilton Hotel. The Hague: The Netherlands.
Shariati, M., Ramli Slong, N. H., & Arabnejad Khanouki, M. M. (2012c). Experimental assessment of channel shear connectors under monotonic and fully reversed cyclic loading in high strength concrete. Materials & Design, 34, 325–331.
Shariati, M., Ramli Sulong, N. H., Arabnejad Khanouki, M. M., & Mahoutian, M. (2011a). Shear resistance of channel shear connectors in plain, reinforced and lightweight concrete. Scientific Research and Essays, 6, 977–983.
Shariati, M., Ramli Sulong, N. H., Arabnejad Khanouki, M. M., & Shariati, A. (2011b). Experimental and numerical investigations of channel shear connectors in high strength concrete. In Proceedings of the 2011 world congress on advances in structural engineering and mechanics (ASEM’11+), Seoul, South Korea.
Shariati, M., Ramli Sulong, N. H., Sinaei, H., Arabnejad Khanouki, M. M., & Shafigh, P. (2011c). Behavior of channel shear connectors in normal and light weight aggregate concrete (experimental and analytical study). Advanced Materials Research, 168, 2303–2307.
Shariati, M., Ramli Sulong, N. H., Suhatril, M., Shariati, A., Arabnejad Khanouki, M. M., & Sinaei, H. (2013). Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading. Construction and Building Materials, 38, 582–593.
Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2, 256–262.
Wang, X., & Han, M. (2014). Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing, 145, 90–97.
Wang, D. D., Wang, R., & Yan, H. (2014). Fast prediction of protein–protein interaction sites based on extreme learning machines. Neurocomputing, 128, 258–266.
Wong, P. K., Wong, K. I., Vong, C. M., & Cheung, C. S. (2015). Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renewable Energy, 74, 640–647.
Yilmaz, I., & Kaynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38, 5958–5966.
Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296–302.
Acknowledgments
The study presented herein was made possible by the University of Malaya Research Grant, UMRG RP004D-13AET and the University of Malaya Research Grant, UMRG RP004A-13AET. The authors would like to acknowledge the supports.
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The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2, 3, 4, 5]) and authorship manipulation. Meldi Suhatril, Zainah Ibrahim, Maryam Safa, Mahdi Shariati and Shahaboddin Shamshirband do not agree to this retraction. Ali Toghroli has not responded to any correspondence about this retraction.
References
1. Toghroli, A., Suhatril, M., Ibrahim, Z. et al. J Intell Manuf (2018) 29: 1793. https://doi.org/10.1007/s10845-016-1217-y
2. Zarko Cojbasic et al. Surface roughness prediction by extreme learning machine constructed with abrasive water jet Precision Engineering (2016), Vol 43, pp. 86-92 DOI 10.1016/j.precisioneng.2015.06.013
3. Iman Mazinani et al. Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine Entropy (2016) 18(5), 167 DOI 10.3390/e18050167
4. Mohammadian, E., Motamedi, S., Shamshirband, S. et al. Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide Environ Earth Sci (2016) 75: 215. https://doi.org/10.1007/s12665-015-4798-4
5. Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I (2015) An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines. PLoS ONE 10(9): e0138493. https://doi.org/10.1371/journal.pone.0138493
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Toghroli, A., Suhatril, M., Ibrahim, Z. et al. RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf 29, 1793–1801 (2018). https://doi.org/10.1007/s10845-016-1217-y
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DOI: https://doi.org/10.1007/s10845-016-1217-y