Browse > Article
http://dx.doi.org/10.12989/scs.2021.40.3.461

Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models - a new approach  

Luat, Nguyen-Vu (Department of Architectural Engineering, Sejong University)
Shin, Jiuk (Department of Architectural Engineering, Gyeongsang National University)
Han, Sang Whan (Department of Architectural Engineering, Hanyang University)
Nguyen, Ngoc-Vinh (Department of Infrastructure Engineering, Vietnam - Japan University)
Lee, Kihak (Department of Architectural Engineering, Sejong University)
Publication Information
Steel and Composite Structures / v.40, no.3, 2021 , pp. 461-479 More about this Journal
Abstract
This study aims to propose a new intelligence technique of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid system based on one of the evolution algorithm - Genetic Algorithm (GA), fused with a well-known data-driven model of multivariate adaptive regression splines (MARS), namely G-MARS, was proposed and applied. To construct the MARS model, a database of 504 experimental cases was collected from the available literature. The GA was utilized to determine an optimal set of MARS' hyperparameters, to improve the prediction accuracy. The compiled database covered five input variables, including the diameter of the circular cross section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (${\acute{f}}_c$), and the yield strength of the steel tube (fy). A new explicit formulation was derived from MARS in further analysis, and its estimation accuracy was validated against a benchmark model, G-ANN, an artificial neural network (ANN) optimized using the same metaheuristic algorithm. The simulation results in terms of error range and statistical indices indicated that the derived formula had a superior capability in predicting the ultimate capacity of CCFST columns, relative to the G-ANN model and the other existing empirical methods.
Keywords
CCFST; concrete-filled steel tube column; evolutionary hybrid model; genetic algorithm; multivariate adaptive regression spline; ultimate capacity;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Goldberg, D.E. (1989), "Genetic Algorithms in Search, Optimization and Machine Learning", 1st ed. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
2 Cheng, M.Y. and Cao, M.T. (2014), "Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams", Eng. Appl. Artif. Intell., 28, 86-96. https://doi.org/10.1016/j.engappai.2013.11.001.   DOI
3 Dundu, M. (2012), "Compressive strength of circular concrete filled steel tube columns", Thin-Wall. Struct., 56, 62-70. https://doi.org/10.1016/j.tws.2012.03.008.   DOI
4 Australian Standard TM Bridge design Part 6 : Steel and composite construction, (2004).
5 Beck, A.T., de Oliveira, W.L.A., De Nardim, S. and ElDebs, A.L. H.C. (2009), "Reliability-based evaluation of design code provisions for circular concrete-filled steel columns", Eng. Struct., 31(10), 2299-2308. https://doi.org/10.1016/j.engstruct.2009.05.004.   DOI
6 Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T. D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Constr. Build. Mater., 180, https://doi.org/10.1016/j.conbuildmat.2018.05.201.   DOI
7 Chen, Y., Hou, C. and Peng, J., (2019), "Stability study on tenon-connected SHS and CFST columns in modular construction", Steel Compos. Struct., 30(2), 185-199. https://doi.org/10.12989/scs.2019.30.2.185.   DOI
8 Evirgen, B., Tuncan, A. and Taskin, K., (2014), "Structural behavior of concrete filled steel tubular sections (CFT/CFSt) under axial compression", Thin-Wall. Struct., 80, 46-56. https://doi.org/10.1016/j.tws.2014.02.022.   DOI
9 European Committee for Standardization (2004), "Eurocode 4: Design of Composite Steel and Concrete Structures-Part 1-1: General Rules and Rules for Buildings,", London, UK.
10 Friedman, J.H. (1991), "Multivariate Adaptive Regression Splines", Ann. Statics, 19(1), 1-67. https://www.jstor.org/stable/2241837.
11 Giakoumelis, G. and Lam, D. (2004), "Axial capacity of circular concrete-filled tube columns", J. Constr. Steel Res., 60(7), 1049-1068. https://doi.org/10.1016/j.jcsr.2003.10.001.   DOI
12 Ekmekyapar, T. and Al-Eliwi, B.J.M. (2016), "Experimental behaviour of circular concrete filled steel tube columns and design specifications", Thin-Wall. Struct., 105, 220-230. https://doi.org/10.1016/j.tws.2016.04.004.   DOI
13 Zheng, D., Qian, Z., Liu, Y. and Liu, C. (2018), "Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network", Constr. Build. Mater., 158, 614-623. https://doi.org/10.1016/j.conbuildmat.2017.10.056.   DOI
14 Yaseen, Z.M., Tung, M., Kim, S., Bakhshpoori, T. and Deo, R.C. (2018), "Shear strength prediction of steel fi ber reinforced concrete beam using hybrid intelligence models : A new approach", Eng. Struct., 177, 244-255. https://doi.org/10.1016/j.engstruct.2018.09.074.   DOI
15 Luat, N.V., Nguyen, V.Q., Lee, S., Woo, S. and Lee, K. (2020), "An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils", Geoemech. Eng., 21(6), 583-598. https://doi.org/10.12989/gae.2020.21.6.583.   DOI
16 Portoles, J.M., Romero, M.L., Bonet, J.L. and Filippou, F.C. (2011), "Experimental study of high strength concrete-filled circular tubular columns under eccentric loading", J. Constr. Steel Res., 67(4), 623-633. https://doi.org/10.1016/j.jcsr.2010.11.017.   DOI
17 Luat, N.V., Lee, K. and Thai, D.K. (2020), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", 20(5), 385-397. https://doi.org/10.12989/gae.2020.20.5.385.   DOI
18 Luat, N.V., Lee, J., Lee, D.H. and Lee, K. (2020), "GS - MARS method for predicting the ultimate load - carrying capacity of rectangular CFST columns under eccentric loading", Comput. Concret, 25(1), 1-4. https://doi.org/10.12989/cac.2020.25.1.001.   DOI
19 Nguyen, M.S.T., Thai D.K. and Kim, S.E. (2020), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/scs.2020.35.3.415.   DOI
20 Nikdel, P., Hosseinpour, M., Badamchizadeh, M.A. and Akbari, M. A. (2014), "Improved Takagi-Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm", Eng. Appl. Artif. Intell., 33, 12-20. https://doi.org/10.1016/j.engappai.2014.03.009.   DOI
21 Ren, Q., Li, M., Zhang, M., Shen, Y. and Si, W. (2019), "Prediction of ultimate axial capacity of square concrete-filled steel tubular short columns using a hybrid intelligent algorithm", Appl. Sci., 9(14).https://doi.org/10.3390/app9142802.   DOI
22 Standardization E.C. for and Institution B.S. (2004), "Eurocode 4: Design of composite steel and concrete structures. Part 1.1, General rules and rules for buildings".
23 Luat, N.V., Shin, J. and Lee, K. (2020), "Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns", Eng. Comput., 1-30. https://doi.org/10.1007/s00366-020-01115-7.   DOI
24 Moon, J., Kim, J.J., Lee, T.H. and Lee, H.E. (2014), "Prediction of axial load capacity of stub circular concrete-filled steel tube using fuzzy logic", J. Constr. Steel Res., 101, 184-191. https://doi.org/10.1016/j.jcsr.2014.05.011.   DOI
25 Ren, Y. and Bai, G. (2010), "Determination of optimal SVM parameters by using GA/PSO", J. Comput., 5(8), 1160-1168. https://doi.org/10.4304/jcp.5.8.1160-1168.   DOI
26 Guneyisi, E.M., Gultekin, A. and Mermerdas, K. (2016), "Ultimate capacity prediction of axially loaded CFST short columns", Int. J. Steel Struct., 16(1), 99-114. https://doi.org/10.1007/s13296-016-3009-9.   DOI
27 Hayalioglut, M.S. and Degertekini, S.O. (2004), "Genetic algorithm based optimum design of non-linear steel frames with semi-rigid connections", Steel Compos. Struct., 4(6), 453-469. https://doi.org/10.12989/scs.2004.4.6.453.   DOI
28 Wang, Z.B., Tao, Z., Han, L.H., Uy, B., Lam, D. and Kang, W.H. (2017), "Strength, stiffness and ductility of concrete-filled steel columns under axial compression", Eng. Struct., 135, 209-221 https://doi.org/10.1016/j.engstruct.2016.12.049.   DOI
29 Xiong, M.X., Xiong, D.X. and Liew, J.Y.R. (2017b), "Behaviour of steel tubular members infilled with ultra high strength concrete", J. Constr. Steel Res., 138, 168-183. https://doi.org/10.1016/j.jcsr.2017.07.001.   DOI
30 Koopialipoor, M., Fallah, A., Armaghani, D.J., Azizi A. and Mohamad E.T. (2019), "Three hybrid intelligent models in estimating flyrock distance resulting from blasting", Eng. Comput., 35(1), 243-256. https://doi.org/10.1007/s00366-018-0596-4.   DOI
31 Craven, P. and Wahba, G. (1978), "Smoothing noisy data with spline functions", Numer. Math., 31(4), 377-403.https://doi.org/10.1007/BF01404567.   DOI
32 ACI Committee 318, (2014), "Building code requirements for structural concrete and commentary (ACI318-14)", Farmington Hills, MI, USA.
33 Ahmadi, M., Naderpour, H. and Kheyroddin, A. (2014), "Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load", Arch. Civ. Mech. Eng., 14(3), 510-517. https://doi.org/10.1016/j.acme.2014.01.006.   DOI
34 Nobahari, M., Ghasemi, M.R. and Shabakhty, N. (2017), "Truss structure damage identification using residual force vector and genetic algorithm", Steel Compos. Struct., 25(4), 485-496. https://doi.org/10.12989/scs.2017.25.4.485.   DOI
35 AISC Committee (2010), "Specification for Structural Steel Buildings (ANSI/AISC 360-10)", Chicago-Illinois: American Institute of Steel Construction.
36 Alavi, A.H., Gandomi, A.H., Mousavi, M. and Mollahasani, A. (2010), "High-precision modeling of uplift capacity of suction caissons using a hybrid computational method" Geomech. Eng., 2(4), 253-280.https://doi.org/10.12989/gae.2010.2.4.253.   DOI
37 Avci-karatas, C. (2019), "Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS)", Steel Compos. Struct., 33(4), 583-594. https://doi.org/10.12989/scs.2019.33.4.583   DOI
38 Chen, C., Yousif, S.T., Najem, R.M., Abavisani, A., Pham, B.T., Wakil, K., Tonnizam Mohamad, E. and Khorami, M. (2019), "Optimum cost design of frames using genetic algorithms", Steel Compos. Struct., 30(3), 293-304. https://doi.org/10.12989/scs.2019.30.3.293.   DOI
39 Ahmadi, M., Naderpour, H. and Kheyroddin, A., (2017), "ANN Model for Predicting the Compressive Strength of Circular Steel-Confined Concrete", Int. J. Civ. Eng., 15(2), 213-221. https://doi.org/10.1007/s40999-016-0096-0.   DOI
40 Ghannam S., Jawad Y.A. and Hunaiti, Y. (2004), "Failure of lightweight aggregate concrete-filled steel tubular columns." Steel Compos. Struct., 4(1), 1-8. https://doi.org/10.12989/scs.2004.4.1.001.   DOI
41 Gomes, G.F., de Almeida, F.A., Junqueira, D.M., da Cunha, S.S. and Ancelotti, A.C. (2019), "Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods", Eng. Struct., 181(2018), 111-123. https://doi.org/10.1016/j.engstruct.2018.11.081.   DOI
42 Han, L.H.H., Li, W. and Bjorhovde, R. (2014), "Developments and advanced applications of concrete-filled steel tubular (CFST) structures: Members", J. Constr. Steel Res., 100, 211-228. https://doi.org/10.1016/j.jcsr.2014.04.016.   DOI
43 Han, L.H. and An, Y.F. (2014), "Performance of concrete-encased CFST stub columns under axial compression", J. Constr. Steel Res., 93, 62-76.https://doi.org/10.1016/j.jcsr.2013.10.019.   DOI
44 Zhao, H., (2016), "Analysis of seismic behavior of composite frame structures", Steel Compos. Struct., 20(3), 719-729. https://doi.org/10.12989/scs.2016.20.3.719.   DOI
45 Holland, J.H. (1975), "Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence", Oxford, England: U Michigan Press.
46 Li, N., Lu, Y.Y., Li, S. and Liang, H.J. (2015), "Statistical-based evaluation of design codes for circular concrete-filled steel tube columns", Steel Compos. Struct., 18(2), 519-546. https://doi.org/10.12989/scs.2015.18.2.519.   DOI
47 Sarir, P., Shen, S.L., Wang, Z.F., Chen, J., Horpibulsuk, S. and Pham, B.T. (2019), "Optimum model for bearing capacity of concrete-steel columns with AI technology via incorporating the algorithms of IWO and ABC", Eng. Comput., (0123456789). https://doi.org/10.1007/s00366-019-00855-5.   DOI
48 Xiong, M.X., Xiong, D.X. and Liew, J.Y.R. (2017a), "Axial performance of short concrete filled steel tubes with high- and ultra-high- strength materials", Eng. Struct., 136, 494-510. https://doi.org/10.1016/j.engstruct.2017.01.037.   DOI