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Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması

Yıl 2025, Cilt: 13 Sayı: 1, 150 - 164, 30.06.2025
https://doi.org/10.18586/msufbd.1593159

Öz

Bu çalışmada Jaya algoritması (JA), öğretme-öğrenme esaslı optimizasyon (TLBO) ve TLBO’nun çoklu sınıf algoritması (MC-TLBO) algoritmaları kullanılarak çelik çerçevelerin (moment aktaran) performansa dayalı sismik tasarım (PBSD) optimizasyon ile birleştirilerek (PBOSD) yapılmıştır. PBOSD’da göreli ötelenme (katlar arası) oranı ve dayanım sınırlayıcıları altında minimum analiz sayısı ve minimum ağırlığa sahip, deplasman sınırlayıcıları saplayan çelik çerçevelerin elde edilmesi amaçlanmaktadır. Optimizasyon yöntemi olarak TLBO, JA, MC-TLBO’yu kullanan optimum tasarım formülasyonu yazılmış ve OpenSees’den elde edilen sonuçlar göz önüne alınarak performansa dayalı optimum sismik tasarımın icrası gerçekleştirilmiştir. Öne sürülen yöntemin geçerliliği literatürde Geleneksel Tasarım (CD), Karınca Koloni Optimizasyon Algoritması (ACO), Genetik Algoritma ve Yüklü Sistem Algoritması (CSS) yöntemleri kullanılarak optimum sismik tasarımı yapılmış iki adet düzlem çerçeve için JA, TLBO ve MC-TLBO test edilmiştir. Elde edilen sonuçlar minimum ağırlık ve minimum analiz sayısı gibi kriterler açısından kıyaslanmıştır. Bu çalışma sonucunda TLBO, JA, MC-TLBO ile elde edilen optimum sonuçlar CD, ACO, GA ve CSS algoritmaları ile karşılaştırılarak sonuçlar ortaya konulmuştur. Bu sonuçlar MCTLBO’nun diğer algoritmalara kıyasla daha iyi tasarımlar üretttiği belirlenmiştir.

Kaynakça

  • [1] Khoshnoud, H.R. and K. Marsono, Assessment of FEMA356 nonlinear static procedure and modal pushover analysis for seismic evaluation of buildings. Structural engineering and mechanics: An international journal, 2012. 41(2): p. 243-262.
  • [2] Committee, S.V., Performance-based seismic engineering. Structural Engineers Association of California, Sacramento, California, 1995.
  • [3] ATC, S., Evaluation and retrofit of concrete buildings, Rep. ATC-40, Applied Technology Council, Redwood City, California, 1996.
  • [4] Council, B.S.S. and A.T. Council, NEHRP guidelines for the seismic rehabilitation of buildings. Vol. 1. 1997: Federal Emergency Management Agency.
  • [5] FEMA 356, F.E., Prestandard and commentary for the seismic rehabilitation of buildings. Federal Emergency Management Agency: Washington, DC, USA, 2000.
  • [6] Fema, A., 440, Improvement of nonlinear static seismic analysis procedures. FEMA-440, Redwood City, 2005. 7(9): p. 11.
  • [7] ASCE, Minimum design loads for buildings and other structures. 2010, ASCE Reston, VA.
  • [8] Hasan, R., L. Xu, and D. Grierson, Push-over analysis for performance-based seismic design. Computers & structures, 2002. 80(31): p. 2483-2493.
  • [9] Saafan, S.A., Nonlinear behavior of structural plane frames. Journal of the Structural Division, 1963. 89(4): p. 557-579.
  • [10] Soleimani Amiri, F., G. Ghodrati Amiri, and H. Razeghi, Estimation of seismic demands of steel frames subjected to near‐fault earthquakes having forward directivity and comparing with pushover analysis results. The Structural Design of Tall and Special Buildings, 2013. 22(13): p. 975-988.
  • [11] Chopra, A.K. and R.K. Goel, A modal pushover analysis procedure for estimating seismic demands for buildings. Earthquake engineering & structural dynamics, 2002. 31(3): p. 561-582.
  • [12] Krawinkler, H. Pushover analysis: why, how, when, and when not to use it. in Proceedings of the 65th Annual Convention of the Structural Engineers Association of California. 1996.
  • [13] Mirjalili, M. and F. Rofooei, The modified dynamic‐based pushover analysis of steel moment resisting frames. The Structural Design of Tall and Special Buildings, 2017. 26(12): p. e1378.
  • [14] Holland, J.H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. 1992: MIT press.
  • [15] Dorigo, M., V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics), 1996. 26(1): p. 29-41.
  • [16] Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in MHS'95. Proceedings of the sixth international symposium on micro machine and human science. 1995. Ieee.
  • [17] Kaveh, A. and V.R. Mahdavi, Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 2014. 139: p. 18-27.
  • [18] Kaveh, A. and S. Talatahari, Anovel heuristic optimization method: charged system search. Acta mechanica, 2010. 213(3): p. 267-289.
  • [19] Yang, X.-S. Firefly algorithms for multimodal optimization. in International symposium on stochastic algorithms. 2009. Springer.
  • [20] Kaveh, A., et al., Performance-based seismic design of steel frames using ant colony optimization. Journal of Constructional Steel Research, 2010. 66(4): p. 566-574.
  • [21] Gholizadeh, S. and R.K. Moghadas, Performance-based optimum design of steel frames by an improved quantum particle swarm optimization. Advances in Structural Engineering, 2014. 17(2): p. 143-156.
  • [22] Talatahari, S., et al., Optimum Performance‐Based Seismic Design Using a Hybrid Optimization Algorithm. Mathematical Problems in Engineering, 2014. 2014(1): p. 693128.
  • [23] Veladi, H., Performance‐Based Seismic Design of Steel Frames Utilizing Colliding Bodies Algorithm. The Scientific World Journal, 2014. 2014(1): p. 240952.
  • [24] Kaveh, A. and A. Nasrollahi, Performance-based seismic design of steel frames utilizing charged system search optimization. Applied Soft Computing, 2014. 22: p. 213-221.
  • [25] Ghasemof, A., et al. Multi-objective optimal design of steel MRF buildings based on life-cycle cost using a swift algorithm. in Structures. 2021. Elsevier.
  • [26] Mohammadi, R.K. and A.H. Sharghi, On the optimum performance-based design of eccentrically braced frames. Steel Compos. Struct, 2014. 16(4): p. 357-374.
  • [27] Gholizadeh, S., Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network. Advances in Engineering Software, 2015. 81: p. 50-65.
  • [28] Mansouri, I., et al., Performance based design optimum of CBFs using bee colony algorithm. Steel and Composite Structures, 2018. 27(5): p. 613-622.
  • [29] Degertekin, S.O. and H. Tutar. Performance-based optimum seismic design of planar steel frames using the jaya algorithm. in 4th Eurasian conference on civil and environmental engineering. Istanbul (Turkey). 2019.
  • [30] Degertekin, S.O., H. Tutar, and L. Lamberti, School-based optimization for performance-based optimum seismic design of steel frames. Engineering with Computers, 2021. 37(4): p. 3283-3297.
  • [31] Degertekin, S.O. and H. Tutar, Optimized seismic design of planar and spatial steel frames using the hybrid learning based jaya algorithm. Advances in Engineering Software, 2022. 171: p. 103172.
  • [32] Rodríguez, C.A., et al., Comparative analysis and evaluation of seismic response in structures: Perspectives from non-linear dynamic analysis to pushover analysis. Applied Sciences, 2024. 14(6): p. 2504.
  • [33] Tschemmernegg, F., On the nonlinear behaviour of joints in steel frames. Connections in steel structures: Behaviour, strength and design, 1988: p. 158-165.
  • [34] McKenna, F., et al., OpenSees. University of California, Berkeley: nd, 2010.
  • [35] Rao, R., Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 2016. 7(1): p. 19-34.
  • [36] Degertekin, S.O., L. Lamberti, and I.B. Ugur, Sizing, layout and topology design optimization of truss structures using the Jaya algorithm. Applied soft computing, 2018. 70: p. 903-928.
  • [37] Rao, R.V., V.J. Savsani, and D.P. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design, 2011. 43(3): p. 303-315.
  • [38] Dede, T. and Y. Ayvaz, Structural optimization with teaching-learning-based optimization algorithm. Structural engineering and mechanics: An international journal, 2013. 47(4): p. 495-511.
  • [39] Degertekin, S. and M. Hayalioglu, Sizing truss structures using teaching-learning-based optimization. Computers & Structures, 2013. 119: p. 177-188.
  • [40] Shallan, O., H.M. Maaly, and O. Hamdy, A developed design optimization model for semi-rigid steel frames using teaching-learning-based optimization and genetic algorithms. Structural engineering and mechanics: An international journal, 2018. 66(2): p. 173-183.
  • [41] Farshchin, M., C. Camp, and M. Maniat, Multi-class teaching–learning-based optimization for truss design with frequency constraints. Engineering Structures, 2016. 106: p. 355-369.
  • [42] Committee, A., Specification for structural steel buildings (ANSI/AISC 360-16). 2016, American Institute of Steel Construction Chicago.
  • [43] Črepinšek, M., S.-H. Liu, and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 2013. 45(3): p. 1-33.
  • [44] Matlab, S., “Matlab,” MathWorks. Natick, MA, 2012.

Comparison of using Multi-Class Teaching-Learning Based Optimization (MC-TLBO) algorithm to perform performance-based optimum seismic design (PBOSD) of planar steel frames

Yıl 2025, Cilt: 13 Sayı: 1, 150 - 164, 30.06.2025
https://doi.org/10.18586/msufbd.1593159

Öz

In this study, the performance based seismic design (PBSD) of steel frames (moment transferring) is performed by combining optimization (PBOSD) with Jaya algorithm (JA), teaching-learning based optimization (TLBO) and TLBO's multi-class algorithm (MC-TLBO). In PBOSD, it is aimed to obtain steel frames with minimum analysis number and minimum weight under the relative drift (interstory) ratio and strength constraints, and to set displacement constraints. The optimum design formulation using TLBO, JA, MC-TLBO as optimization methods is written and the performance based optimum seismic design is implemented by considering the results obtained from OpenSees. The validity of the proposed method is tested for two plane frames whose optimum seismic design is made using Conventional Design (CD), Ant Colony Optimization Algorithm (ACO), Genetic Algorithm and Loaded System Algorithm (CSS) methods in the literature. The obtained results were compared in terms of criteria such as minimum weight and minimum number of analyses. As a result of this study, the optimum results obtained with TLBO, JA, MC-TLBO were compared with CD, ACO, GA and CSS algorithms and the results were presented. These results determined that MCTLBO produced better designs compared to other algorithms.

Kaynakça

  • [1] Khoshnoud, H.R. and K. Marsono, Assessment of FEMA356 nonlinear static procedure and modal pushover analysis for seismic evaluation of buildings. Structural engineering and mechanics: An international journal, 2012. 41(2): p. 243-262.
  • [2] Committee, S.V., Performance-based seismic engineering. Structural Engineers Association of California, Sacramento, California, 1995.
  • [3] ATC, S., Evaluation and retrofit of concrete buildings, Rep. ATC-40, Applied Technology Council, Redwood City, California, 1996.
  • [4] Council, B.S.S. and A.T. Council, NEHRP guidelines for the seismic rehabilitation of buildings. Vol. 1. 1997: Federal Emergency Management Agency.
  • [5] FEMA 356, F.E., Prestandard and commentary for the seismic rehabilitation of buildings. Federal Emergency Management Agency: Washington, DC, USA, 2000.
  • [6] Fema, A., 440, Improvement of nonlinear static seismic analysis procedures. FEMA-440, Redwood City, 2005. 7(9): p. 11.
  • [7] ASCE, Minimum design loads for buildings and other structures. 2010, ASCE Reston, VA.
  • [8] Hasan, R., L. Xu, and D. Grierson, Push-over analysis for performance-based seismic design. Computers & structures, 2002. 80(31): p. 2483-2493.
  • [9] Saafan, S.A., Nonlinear behavior of structural plane frames. Journal of the Structural Division, 1963. 89(4): p. 557-579.
  • [10] Soleimani Amiri, F., G. Ghodrati Amiri, and H. Razeghi, Estimation of seismic demands of steel frames subjected to near‐fault earthquakes having forward directivity and comparing with pushover analysis results. The Structural Design of Tall and Special Buildings, 2013. 22(13): p. 975-988.
  • [11] Chopra, A.K. and R.K. Goel, A modal pushover analysis procedure for estimating seismic demands for buildings. Earthquake engineering & structural dynamics, 2002. 31(3): p. 561-582.
  • [12] Krawinkler, H. Pushover analysis: why, how, when, and when not to use it. in Proceedings of the 65th Annual Convention of the Structural Engineers Association of California. 1996.
  • [13] Mirjalili, M. and F. Rofooei, The modified dynamic‐based pushover analysis of steel moment resisting frames. The Structural Design of Tall and Special Buildings, 2017. 26(12): p. e1378.
  • [14] Holland, J.H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. 1992: MIT press.
  • [15] Dorigo, M., V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics), 1996. 26(1): p. 29-41.
  • [16] Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in MHS'95. Proceedings of the sixth international symposium on micro machine and human science. 1995. Ieee.
  • [17] Kaveh, A. and V.R. Mahdavi, Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 2014. 139: p. 18-27.
  • [18] Kaveh, A. and S. Talatahari, Anovel heuristic optimization method: charged system search. Acta mechanica, 2010. 213(3): p. 267-289.
  • [19] Yang, X.-S. Firefly algorithms for multimodal optimization. in International symposium on stochastic algorithms. 2009. Springer.
  • [20] Kaveh, A., et al., Performance-based seismic design of steel frames using ant colony optimization. Journal of Constructional Steel Research, 2010. 66(4): p. 566-574.
  • [21] Gholizadeh, S. and R.K. Moghadas, Performance-based optimum design of steel frames by an improved quantum particle swarm optimization. Advances in Structural Engineering, 2014. 17(2): p. 143-156.
  • [22] Talatahari, S., et al., Optimum Performance‐Based Seismic Design Using a Hybrid Optimization Algorithm. Mathematical Problems in Engineering, 2014. 2014(1): p. 693128.
  • [23] Veladi, H., Performance‐Based Seismic Design of Steel Frames Utilizing Colliding Bodies Algorithm. The Scientific World Journal, 2014. 2014(1): p. 240952.
  • [24] Kaveh, A. and A. Nasrollahi, Performance-based seismic design of steel frames utilizing charged system search optimization. Applied Soft Computing, 2014. 22: p. 213-221.
  • [25] Ghasemof, A., et al. Multi-objective optimal design of steel MRF buildings based on life-cycle cost using a swift algorithm. in Structures. 2021. Elsevier.
  • [26] Mohammadi, R.K. and A.H. Sharghi, On the optimum performance-based design of eccentrically braced frames. Steel Compos. Struct, 2014. 16(4): p. 357-374.
  • [27] Gholizadeh, S., Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network. Advances in Engineering Software, 2015. 81: p. 50-65.
  • [28] Mansouri, I., et al., Performance based design optimum of CBFs using bee colony algorithm. Steel and Composite Structures, 2018. 27(5): p. 613-622.
  • [29] Degertekin, S.O. and H. Tutar. Performance-based optimum seismic design of planar steel frames using the jaya algorithm. in 4th Eurasian conference on civil and environmental engineering. Istanbul (Turkey). 2019.
  • [30] Degertekin, S.O., H. Tutar, and L. Lamberti, School-based optimization for performance-based optimum seismic design of steel frames. Engineering with Computers, 2021. 37(4): p. 3283-3297.
  • [31] Degertekin, S.O. and H. Tutar, Optimized seismic design of planar and spatial steel frames using the hybrid learning based jaya algorithm. Advances in Engineering Software, 2022. 171: p. 103172.
  • [32] Rodríguez, C.A., et al., Comparative analysis and evaluation of seismic response in structures: Perspectives from non-linear dynamic analysis to pushover analysis. Applied Sciences, 2024. 14(6): p. 2504.
  • [33] Tschemmernegg, F., On the nonlinear behaviour of joints in steel frames. Connections in steel structures: Behaviour, strength and design, 1988: p. 158-165.
  • [34] McKenna, F., et al., OpenSees. University of California, Berkeley: nd, 2010.
  • [35] Rao, R., Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 2016. 7(1): p. 19-34.
  • [36] Degertekin, S.O., L. Lamberti, and I.B. Ugur, Sizing, layout and topology design optimization of truss structures using the Jaya algorithm. Applied soft computing, 2018. 70: p. 903-928.
  • [37] Rao, R.V., V.J. Savsani, and D.P. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design, 2011. 43(3): p. 303-315.
  • [38] Dede, T. and Y. Ayvaz, Structural optimization with teaching-learning-based optimization algorithm. Structural engineering and mechanics: An international journal, 2013. 47(4): p. 495-511.
  • [39] Degertekin, S. and M. Hayalioglu, Sizing truss structures using teaching-learning-based optimization. Computers & Structures, 2013. 119: p. 177-188.
  • [40] Shallan, O., H.M. Maaly, and O. Hamdy, A developed design optimization model for semi-rigid steel frames using teaching-learning-based optimization and genetic algorithms. Structural engineering and mechanics: An international journal, 2018. 66(2): p. 173-183.
  • [41] Farshchin, M., C. Camp, and M. Maniat, Multi-class teaching–learning-based optimization for truss design with frequency constraints. Engineering Structures, 2016. 106: p. 355-369.
  • [42] Committee, A., Specification for structural steel buildings (ANSI/AISC 360-16). 2016, American Institute of Steel Construction Chicago.
  • [43] Črepinšek, M., S.-H. Liu, and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 2013. 45(3): p. 1-33.
  • [44] Matlab, S., “Matlab,” MathWorks. Natick, MA, 2012.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Matematikte Optimizasyon, Makine Mühendisliğinde Optimizasyon Teknikleri
Bölüm Araştırma Makalesi
Yazarlar

Hikmet Tutar 0000-0002-1440-7659

Erken Görünüm Tarihi 25 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 28 Kasım 2024
Kabul Tarihi 8 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 1

Kaynak Göster

APA Tutar, H. (2025). Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması. Mus Alparslan University Journal of Science, 13(1), 150-164. https://doi.org/10.18586/msufbd.1593159
AMA Tutar H. Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması. MAUN Fen Bil. Dergi. Haziran 2025;13(1):150-164. doi:10.18586/msufbd.1593159
Chicago Tutar, Hikmet. “Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması”. Mus Alparslan University Journal of Science 13, sy. 1 (Haziran 2025): 150-64. https://doi.org/10.18586/msufbd.1593159.
EndNote Tutar H (01 Haziran 2025) Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması. Mus Alparslan University Journal of Science 13 1 150–164.
IEEE H. Tutar, “Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması”, MAUN Fen Bil. Dergi., c. 13, sy. 1, ss. 150–164, 2025, doi: 10.18586/msufbd.1593159.
ISNAD Tutar, Hikmet. “Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması”. Mus Alparslan University Journal of Science 13/1 (Haziran2025), 150-164. https://doi.org/10.18586/msufbd.1593159.
JAMA Tutar H. Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması. MAUN Fen Bil. Dergi. 2025;13:150–164.
MLA Tutar, Hikmet. “Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması”. Mus Alparslan University Journal of Science, c. 13, sy. 1, 2025, ss. 150-64, doi:10.18586/msufbd.1593159.
Vancouver Tutar H. Düzlemsel Çelik Çerçevelerin Performansa Dayalı Optimum Sismik Tasarımının Çok Sınıflı Algoritması MC-TLBO Kullanılarak Karşılaştırmalarının Yapılması. MAUN Fen Bil. Dergi. 2025;13(1):150-64.