In this study, different modelling techniques such as multiple regression and adaptive neuro-fuzzy inference system (ANFIS) are used for predicting the ultimate pure bending of concrete-filled steel tubes (CFTs). The behaviour of CFT under pure bending is complex and highly nonlinear; therefore, forward modelling techniques can have considerable limitations in practical situations where fast and reliable solutions are required. Linear multiple regression (LMR), nonlinear multiple regression (NLMR) and ANFIS models were trained and checked using a large database that was constructed and populated from the literature. The database comprises 72 pure bending tests conducted on fabricated and cold-formed tubes filled with concrete. Out of 72 tests, 48 tests were conducted by the second author. Input variables for the models are the same with those used by existing codes and practices such as the tube thickness, tube outside diameter, steel yield strength, strength of concrete and shear span. A practical application example, showing the translation of constructed ANFIS model into design equations suitable for hand calculations, was provided. A sensitivity analysis was conducted on ANFIS and multiple regression models. It was found that the ANFIS model is more sensitive to change in input variables than LMR and NLMR models. Predictions from ANFIS models were compared with those obtained from LMR, NLMR, existing theory and a number of available codes and standards. The results indicate that the ANFIS model is capable of predicting the ultimate pure bending of CFT with a high degree of accuracy and outperforms other common methods.
ANFIS Modelling Computing Circular tubes Pure bending Concrete-filled tube
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The authors declare that there is no conflict of interest.
Tomii M (1991) Ductile and strong columns composed of steel tube, in filled concrete and longitudinal steel bars. In: Wayabash M (ed) Proceedings of the third international conference on steel and composite structures, Fukuoka, Japan, pp 39–66Google Scholar
Trezona J, Warner R (2000) Design of slender concrete-filled circular steel tubular columns. Aust Civil Eng J 39:341–352Google Scholar
Shaker-Khalil H (1993) Push out strength of concrete-filled steel hollow sections. Struct Eng 71:230–234Google Scholar
Ichinohe Y, Matsutani T, Nakajima M et al (1991) Elasto-plastic behavior of concrete filled steel circular columns. In: Proceedings of the 3rd international conference on steel concrete composite and structuresGoogle Scholar
Wheeler A, Bridge R (2004) The behavior of circular concrete-filled thin-walled steel tubes in flexure. In: Proceedings of the fifth international conference on composite construction in steel and concreteGoogle Scholar
Lennie AGR, Mervyn JK, Jim N, Tasnim H (2008) Reversal cyclic testing of full scale pipe piles. Technical report no. IS-08-13, Constructed Facilities Laboratory, North Carolina State University, Raleigh, USAGoogle Scholar
SPP20 IBM S (2011) SPSS statistics for windows. IBM Corp, ArmonkGoogle Scholar
Ćirović G, Pamučar D (2013) Decision support model for prioritizing railway level crossings for safety improvements: application of the adaptive neuro-fuzzy system. Expert Syst Appl 40:2208–2223. doi:10.1016/j.eswa.2012.10.041CrossRefGoogle Scholar
Pamučar D, Ljubojevič S, Kostadinovič D, Dorovič B (2016) Cost and risk aggregation in multi-objective route planning for hazardous materials transportation: a neuro-fuzzy and artificial bee colony approach. Expert Syst Appl 65:1–15. doi:10.1016/j.eswa.2016.08.024CrossRefGoogle Scholar
Pamučar D, Vasin L, Atanaskovič P, Miličič M (2016) Planning the city logistics terminal location by applying the green p-median model and type-2 neurofuzzy network. Comput Intell Neurosci. doi:10.1155/2016/6972818Google Scholar