Beton-dolgulu çelik tüplü kompozit kısa kolonların nihai yük taşıma kapasitesinin ilgililik vektör makinesine dayalı tahmini için modelleme yaklaşımı

Yıl 2021, , 615 - 626, 27.07.2021

Çiğdem Avcı-karataş

https://doi.org/10.28948/ngumuh.759297

Cited By: 4

Öz

Bu makalede, dairesel kesitli beton-dolgulu çelik tüplü kompozit kısa kolonların eksenel basınç yükleri altındaki nihai yük taşıma kapasitesini tahmin etmekte ilgililik vektör makinesinin (İVM) uygulanabilirliği incelenmiştir. Destek vektör makinesinin bir eklentisi olarak İVM, regresyon ve sınıflandırmada sağlam çözümler elde etmek için Bayesyen yaklaşımını kullanmaktadır. MATLAB yazılımı ve 150 adet daha önceki çalışmalarda sunulan kapsamlı deneysel veriler kullanılarak ve bu verilerin uygun şekilde düzenlenmesiyle, dairesel kesitli beton-dolgulu çelik tüplü kompozit kısa kolonların nihai yük taşıma kapasitesini tahmin etmek için bir model geliştirilmiştir. Verilerin düzenleme ve doğrulama için gruplandırılmasında azami özen gösterilmiştir. Sırasıyla, düzenleme için yaklaşık %80 veri seti ve doğrulama için %20 veri seti kullanılmıştır. Sonuçlar, beton-dolgulu çelik tüplü kompozit kolon elemanının tahmini nihai eksenel basınç yük taşıma kapasitesinin, ilgili deneysel verilerle kıyaslanabilir olduğunu ve aradaki yüzde farkının yaklaşık ∓%11 olduğunu göstermektedir.

Anahtar Kelimeler

Beton-dolgulu çelik tüplü kompozit kısa kolonlar, Nihai eksenel yük taşıma kapasitesi, İlgililik vektör makinesi (İVM), Lineer olmayan regresyon algoritması

Modeling approach for estimation of ultimate load capacity of concrete-filled steel tube composite stub columns based on relevance vector machine

Öz

In this paper, the applicability of relevance vector machine (RVM) has been explored to predict the ultimate axial load capacity of concrete-filled steel tube composite stub columns (CFSTCSCs) with circular sections under axial compression loadings. As an extension of support vector machine, RVM employs Bayesian inference to achieve parsimonious solutions for regression and classification. By using MATLAB software and 150 comprehensive experimental data presented in the previous studies, a model to predict the ultimate load of circular CFSTCSCs was developed by properly training the data. Utmost care has been taken in grouping the data for training and validation. About 80% dataset for training and 20% dataset for validation have been used, respectively. The results show that the predicted ultimate axial compression load capacity of CFSTCSC members is comparable with that of the corresponding experimental data and the percentage difference is about ∓11%.

Anahtar Kelimeler

Concrete-filled steel tube composite stub columns (CFSTCSCs), Ultimate axial load capacity, Relevance vector machine (RVM), Nonlinear regression algorithm

Kaynakça

• K. Sakino, Y. Sun, Steel jacketing for improvement of column strength and ductility. Proceedings of the 12th World Conference on Earthquake Engineering, New Zealand, February, 2000.
• O. I. Abdelkarim, A. Gheni, S. Anumolu, S. Wang, M. ElGawady, Hollow-core FRP-concrete-steel bridge columns under extreme loading. Report No. cmr15-008; Missouri Department of Transportation Research, Development and Technology, Missouri University of Science and Technology, MO, USA, 2015.
• L. H. Han, G. H. Yao, Experimental behaviour of thin-walled hollow structural steel (HSS) columns filled with self-consolidating concrete (SCC). Thin-Walled Struct., 42(9), 1357–1377, 2004. https://doi.org/ 10.1016/j.tws.2004.03.016.
• L. H. Han, G. H. Yao, X. L. Zhao, Tests and calculations for hollow structural steel (HSS) stub columns filled with self-consolidating concrete (SCC). J. Constr. Steel Res., 61(9), 1241–1269, 2005. https://doi.org/10.1016/j.jcsr.2005.01.004.
• D. L. Liu, W. M. Gho, J Yuan, Ultimate capacity of high-strength rectangular concrete-filled steel hollow section stub columns. J. Constr. Steel Res., 59(12), 1499–1515, 2003. https://doi.org/10.1016/S0143-974X(03)00106-8.
• D. L. Liu, W. M. Gho, Axial load behaviour of high-strength rectangular concrete filled steel tubular stub columns. Thin-Walled Struct., 43(8), 1131–1142, 2005. https://doi.org/10.1016/j.tws.2005.03.007.
• D. M. Lue, J. L. Liu, T. Yen, Experimental study on rectangular CFST columns with high-strength concrete. J. Constr. Steel Res., 63(1), 37–44, 2007. https://doi.org10.1016/j.jcsr.2006.03.007.
• Q. Yu, Z. Tao, Y. X. Wu, Experimental behaviour of high performance concrete filled steel tubular columns. Thin-Walled Struct., 46(4), 362–370, 2008. https://doi.org/10.1016/j.tws.2007.10.001.
• B. Uy, Strength of short concrete filled high strength steel box columns. J. Constr. Steel Res., 57(2), 113–134, 2001. https://doi.org/10.1016/S0143974X(00 )00014-6.
• F. Aslani, B. Uy, Z. Tao, F. Mashiri, Behaviour and design composite columns incorporating compact high-strength steel plates. J. Constr. Steel Res., 107, 94–110, 2015. https://doi.org/10.1016/j.jcsr.2015.01.005.
• ANSI/AISC-360-10, Specification for Structural Steel Buildings. Illinois 60601-1802, American Institute of Steel Construction, Chicago, 2010. https://www.aisc.org/Specification-for-Structural-Steel-Buildings-ANSIAISC-360-16-1.
• Eurocode 4: EN 1994-1-1 (2004) (English): Design of composite steel and concrete structures – Part 1-1: General rules and rules for buildings [Authority: The European Union per Regulation 305/2011, Directive 98/34/EC, Directive 2004/18/EC]. https://eurocodes.jrc .ec.europa.eu/showpage.php?id=134.
• P. Yuvaraj, A. R. Murthy, N. R. Iyer, P. Samui, S. K. Sekar, Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams. Comput. Mater. Contin., 36(1), 73–97, 2013. https://doi.org/ 10.3970/cmc.2013.036.073.
• P. Yuvaraj, A. R. Murthy, N. R. Iyer, P. Samui, S. K. Sekar, Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine. Int. J. Damage Mech., 23(7), 979–1004, 2014. https://doi.org/10.1177/ 1056789514520796.
• S. Dutta, A. R. Murthy, D. Kim, P. Samui, Prediction of compressive strength of self-compacting concrete using intelligent computational modelling. Comput. Mater. Contin., 53(2), 157-174, 2017. https://doi.org/10.3970/cmc.2017.053.167.
• J. Kaur, K. Kaur, A fuzzy approach for an IoT-based automated employee performance appraisal. Comput. Mater. Contin., 53(1), 23–36, 2017. https://doi.org/10.3970/cmc.2017.053.024.
• A. R. Murthy, S. Vishnuvardhan, M. Saravanan, P. Gandhi, Relevance vector based approach for the prediction of stress intensity factor for the pipe with circumferential crack under cyclic loading. Struct. Eng. Mech., 72(1), 31–41, 2019. https://doi.org/10.12989/ sem.2019.72.1.031.
• P. K. Prasanna, A. R. Murthy, K. Srinivas, Prediction of compressive strength of GGBS based concrete using RVM. Struct. Eng. Mech., 68(6), 691–700, 2018. https://doi.org/10.12989/sem.2018.68.6.691.
• C. Avci-Karatas, Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS). Steel Compos. Struct., 33(4), 583–594, 2019. https://doi.org/10.12989/scs.2019.33.4.583.
• A. Gholampour, I. Mansouri, O. Kisi, T. Ozbakkaloglu, Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Comput. Appl., 32, 295–308, 2020. https://doi.org/10.1007/s00521-018-3630-y.
• M. E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res., 1, 211–244, 2001.
• M. E. Tipping, The relevance vector machine. In S. A. Solla, T. K. Leen, and K.-R. Muller, editors, Advances in Neural Information Processing Systems, 12, 652–658, 2000.
• L. Wei, Y. Yang, R. M. Nishikawa, M. N. Wernick, A. Edwards, Relevance vector machine for automatic detection of clustered micro-calcifications. IEEE Transactions on Medical Imaging, 24(10), 1278–1285, 2005. https://doi.org/10.1109/TMI.2005.855435.
• S. K. Das, P. Samui, Prediction of liquefaction potential based on CPT data: A relevance vector machine approach. Proceedings of the 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), October, 2008, Goa, India, 2008.
• W. Caesarendra, A. Widodo, B. S. Yang, Application of relevance vector machine and logistic regression for machine degradation assessment. J. Mech. Syst. Signal Process., 24, 1161–1171, 2009. https://doi.org/10.1016 /j.ymssp.2009.10.011.
• X. Wang, M. Ye, C. J. Duanmu, Classification of data from electronic nose using relevance vector machines. Sens. Actuators B Chem., 140(1), 143–148, 2009. https://doi.org/10.1016/j.snb.2009.04.030.
• K. Liu, Z. Xu, Traffic flow prediction of highway based on wavelet relevance vector machine. J. Inf. Comput. Sci., 8(9), 1641–1647, 2011.
• M. H. Stanikzai, S. Elias, R. Rupakhety, Seismic response mitigation of base-isolated buildings. Appl. Sci., 10(4), 1230, 2020. https://doi.org/10.3390/ app10041230.
• H. Dehghani, I. Mansouri, A. Farzampour, J. W. Hu, Improved homotopy perturbation method for geometrically nonlinear analysis of space trusses, Appl. Sci., 10(8), 2987, 2020. https://doi.org/10.3390/ app10082987.
• N. J. Gardener, R. Jacobson, Structural behavior of concrete filled steel tubes. J. Am. Concr. Inst., 64(7), 404–413, 1967.
• N. J. Gardener, Use of spiral welded steel tubes in pipe columns. J. Am. Concr. Inst., 65(11), 937–942, 1968.
• M. Tomii, K. Yoshimura, Y. Morishita, Experimental studies on concrete filled steel tubular stub columns under concentric loading. Proceedings of the International Colloquium on Stability of Structures under Static and Dynamic Loads, Washington, USA, May, 718–741, 1977.
• K. Sakino, H. Hayashi, Behavior of concrete filled steel tubular stub columns under concentric loading. Proceedings of the 3rd International Conference on Steel Concrete Composite Structures, Fukuoka, Japan, September, 25–30, 1991.
• M. D. O’Shea, R. Q. Bridge, Tests of thin-walled concrete-filled steel tubes. Proceedings of the 12th International Specialty Conference on Cold-Formed Steel Structures, St. Louis, USA, October, 399–419, 1994. https://scholarsmine.mst.edu/isccss/12iccfss/ 12iccfss-session7/3.
• M. D. O'Shea, R. Q. Bridge, Tests on circular thin-walled steel tubes filled with medium and high strength concrete. Australian Civil Engineering Transaction, 40, 15–27, 1998.
• S. P. Schneider, Axially loaded concrete-filled steel tubes. J. Struct. Eng., 124(10), 1125–1138, 1998. https://doi.org/10.1061/(ASCE)07339445(1998)124:10(1125).
• K. F. Tan, X. C. Pu, S. H. Cai, Study on mechanical properties of extra-high strength concrete encased in steel tubes, J. Build. Struct., 20(1), 10–15, 1999. http://manu25.magtech.com.cn/Jwk3_jzjgxb.
• T. Yamamoto, J. Kawaguchi, S. Morino, Experimental study of scale effects on the compressive behavior of short concrete-filled steel tube columns, Proceedings of the United Engineering Foundation Conference on Composite Construction in Steel and Concrete IV (AICE), Banff, Canada, June, 879–891, 2000. https://doi.org/10.1061/40616(281)76.
• C. S. Huang, Y. K. Yeh, G. Y. Liu, H. T. Hu, K. C. Tsai, Y.T. Weng, S. H. Wang, M. H. Wu, Axial load behavior of stiffened concrete-filled steel columns. J. Struct. Eng., 128(9), 1222–1230, 2002. https://doi.org/10.1061/(ASCE)07339445(2002)128:9(1222).
• G. Giakoumelis, D. Lam, Axial capacity of circular concrete-filled tube columns, J. Constr. Steel Res., 60(7), 1049–1068, 2004. https://doi.org/10.1016/ j.jcsr.2003.10.001.
• K. Sakino, H., Nakahara, S. Morino, I. Nishiyama, Behavior of centrally loaded concrete-filled steel-tube short columns, J. Struct. Eng., 130(2), 180–188, 2004. https://doi.org/10.1061/(ASCE)07339445(2004)130:2(180).
• P. K. Gupta, S. M. Sarda, M. S. Kumar, Experimental and computational study of concrete filled steel tubular columns under axial loads. J. Constr. Steel Res., 63(2), 182–193, 2007. https://doi.org/10.1016/j.jcsr.2006 .04.004.
• Z. W. Yu, F. X. Ding, C. S. Cai, Experimental behavior of circular concrete filled steel tube stub columns. J. Constr. Steel Res., 63, 165–174, 2007. https://doi.org/10.1016/j.jcsr.2006.03.009.
• W. L.A. de Oliveira, S. de Nardin, A. L. H. de Cresce El Debs, M. K. El Debs, Influence of concrete strength and length/diameter on the axial capacity of CFT columns. J. Constr. Steel Res., 65(12), 2103–2110, 2009. https://doi.org/10.1016/j.jcsr.2009.07.004.
• S. H., Lee, B. Uy, S. H. Kim, Y. H. Choi, S. M. Choi, Behavior of high-strength circular concrete-filled steel tubular (CFST) column under eccentric loading. J. Constr. Steel Res., 67, 1–13, 2011. https://doi.org/10.1016/j.jcsr.2010.07.003.
• M. X., Xiong, D. X. Xiong, J. Y. R. Liew, Axial performance of short concrete filled steel tubes with high- and ultra-high- strength materials. Eng. Struct., 136, 494–510, 2017. https://doi.org/10.1016/ j.engstruct.2017.01.037.
• S. Guler, A. Copur, M. Aydogan, Axial capacity and ductility of circular UHPC-filled steel tube columns. Mag. Concrete Res., 65(15), 898–905, 2013. https://doi.org/10.1680/macr.12.00211.
• S. Guler, A. Copur, M. Aydogan, A comparative study on square and circular high strength concrete-filled steel tube columns. Adv. Steel Constr., 10(2), 234–247, 2014. https://doi.org/10.18057/IJASC.2014.10.2.7.
• L. H., Han, C. C. Hou, Q. L. Wang, Behavior of circular CFST stub columns under sustained load and chloride corrosion. J. Constr. Steel Res., 103, 23–36, 2014. https://doi.org/10.1016/j.jcsr.2014.07.021.
• S. Ghosh, P. P. Mujumdar, Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv. Water Resour., 31(1), 132–146, 2008. https://doi.org/10.1016/j.advwatres.2007.07.005.