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ÇELİK ÇERÇEVELERİN FARKLI STOKASTİK YÖNTEMLER KULLANILARAK OPTİMUM BOYUTLANDIRILMASI

Year 2019, Volume: 7 Issue: 4, 847 - 861, 04.12.2019
https://doi.org/10.36306/konjes.654958

Abstract

Bu çalışmada rijit çelik çerçevelerin Av Arama, Parçacık Sürü ve Büyük Patlama - Büyük Çöküş optimizasyon yöntemlerini temel alan üç farklı optimum tasarım algoritması sunulmuştur. Av Arama optimizasyon algoritması kurt, aslan, yunus gibi hayvanların grupça avlanmalarından esinlenilerek geliştirilmiştir. Grupta yer alan hayvanlar (avcılar) avlarını bir daire içine alır ve yakalarlar. Avcılar, kokularının av tarafından hissedilmemesi için rüzgâra doğru durmazlar. Bu avcılardan her birisi yapısal bir optimizasyon problemi için aday bir çözüm oluşturur. Parçacık Sürü optimizasyon algoritması sürü halinde hareket eden kuş, balık ve böceklerden esinlenerek geliştirilmiş bir yöntemdir. Sürüdeki her hayvan (parçacık) kendi konumunu sürüde bulunan diğer parçacıklara ve kendisinin bir önceki konumuna göre düzenler. Hedefe ulaşıncaya kadar bu süreç devam eder. Büyük Patlama – Büyük Çöküş optimizasyon algoritması evrenin büyük patlama ve büyük çöküş hipotezinden esinlenilmiştir. Büyük Patlama evresinde Merkez Noktası ya da Kütle Merkezi olarak adlandırılan benzersiz bir nokta üretilmektedir. Algoritma kapsamında Kütle Merkezi yakınlarında yeni adaylar belirlenir, uygunlukları hesaplanır ve içlerinden en iyisi seçilir. Çalışma kapsamında üç katlı üç açıklıklı, beş katlı üç açıklıklı ve yedi katlı üç açıklıklı olmak üzere üç adet çelik çerçevenin bu algoritmalar ile en küçük ağırlıkları bulunacak şekilde optimum boyutlandırılmaları yapılmıştır. Boyutlandırma yapılırken LRFD-AISC (Yük ve Dayanım Katsayılarıyla Tasarım (YDKT)) yönetmeliğindeki sınırlayıcıların sağlanması hedeflenmiştir. Ayrıca, üç teknik birbiriyle kıyaslanmış, Parçacık Sürü algoritması 2 örnekte en iyi sonucu bulması açısından diğerlerine kıyasla daha iyi bir performans sergilemiş, Av Arama algoritması da onu takip etmiştir. Her örnek için en küçük ağırlığı bulan tekniğin sonuçları baz alınarak çerçevelerin SAP2000 analiz programı ile yük altında deplasman analizleri yapılmıştır. Son olarak, çerçevelerin en üst kat yanal deplasmanları belirlenmiş ve yorumlarda bulunulmuştur.

References

  • Arsan, T., 2018, “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”, Journal of Natural and Applied Sciences, Cilt 22, ss. 367-374.
  • Aydoğdu, İ., Akın, A., Saka, M. P., 2016, “Design optimization of real World steel space frames using artificial bee colony algorithm with Levy flight distribution”, Advances in Engineering Software, Volume 92, pp. 1-14.
  • Doğan, E., 2010, “Optimum Design Of Rigid and Semi-Rigid Steel Sway Frames Including Soil- Structure Interaction”, Ph.D. Dissertation, Middle East Technical University, Ankara, Turkey, 262 p.
  • Doğan, E. Saka, M. P., 2012, “Optimum design of unbraced steel frames to the LRFD-AISC code using particle swarm optimization”, Advances in Engineering Software,Volume 46, pp. 27–34.
  • Erdem, R. T., 2015, “Non-linear performance analysis of existing and concentric braced steel structures”, Steel and Composite Structures, Volume19, Issue 1,pp. 59-74.
  • Erol, O. K.,Eksin, I. A.,2006,“A new optimization method: Big Bang – Big Crunch”, Advances in Engineering Software, Volume 37, pp. 106–11.
  • Gücüyen, E., Erdem, R. T., 2014, “Corrosion effects on structural behaviour of jacket type offshore structures”, Gradevinar, Volume 66, Issue 11, pp. 981-986.
  • Kamal, O. El-Mahdy O., Nour, M. El-Komy, G., 2015,“Optimization of Steel Structures Using Particle Swarm Technique”, The Engineering and Scientific Research Journal,pp.1-14.
  • Khalilporazari, S., Khalilporazari, Pasandideh, S. H. R., “Modeling and optimization of multi-item multi-constrained EOQ model for growing items”, Knowledge-Based Systems,Volume 164, pp. 150-162.
  • Khalilporazari, S., Khalilporazari, S., “A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization ofthe surface grinding process”, Engineering Optimization, Volume 49, pp. 878-895.
  • Kaveh, A. Abbasgholiha, H., 2011,“Optimum design of steel sway frames using Big Bang-Big Crunch algorithm”, Asian Journal of Civil Engineering,Volume 12, pp. 293-317.
  • Kaveh, A., Bakhshpoori, T.,2013,“Optimum design of steel frames using Cuckoo Search algorithm with Levy flights”, Struct. Design Tall Spec. Build. Volume22, pp. 1023–1036.
  • Kennedy, J., Eberhart, R., 1995, “Particle swarm optimization”,Proceedings of International Conference Neural Networks, Volume 4, pp. 1942-1948.
  • LRFD-AISC Manual of Steel Construction, 2011,“Load and Resistance Factor Design”, 3rd Edition. American Institute of Steel Construction, USA.
  • Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.,2010,“A novel meta-heuristic optimizationalgorithm inspired by group hunting of animals: Hunting search.” ComputersMathematics with Applications, Vol. 60, pp. 2087-2098.
  • Reynolds, C., W., 1987, “Flocks, herds, and schools: A distributed behavioral model”, ACM Computer Graphics, Volume21, pp. 25–34.
  • Saka, M. P., 2009,“Optimum design of steel sway frames to BS5950 using harmony search algorithm”, Journal of Constructional Steel Research, Volume 65, pp. 36-43.
  • Saka, M. P.,Doğan, E.,Aydoğdu,İ.,2013,“Review and Analysis of Swarm-Intelligence Based Algorithms. Swarm Intelligence and Bio-Inspired Computation”, Theory and Applications, Chapter: 2. Ed: X-S Yang, Z Cui, R. Xiao, A. M. Gandomi, M. Karamanoğlu, Elsevier.
  • SAP 2000 Analysis Reference Manual. Computers and Structures Inc., Berkeley.

Optimum Design of Steel Frames Using Different Stochastic Techniques

Year 2019, Volume: 7 Issue: 4, 847 - 861, 04.12.2019
https://doi.org/10.36306/konjes.654958

Abstract

In this study, three different optimum design algorithms including Hunting Search, Particle Swarm and Big Bang - Big Crunch techniques of rigid steel frames are presented. Hunting Search optimization algorithm is developed by inspiring group hunting of animals such as wolf, lion and dolphin. Animals (predators) in the group circle their hunts and catch them in the end. Predators do not stand towards wind not to felt their smells by prey animals. Each of these predators constitutes a solution for a structural optimization problem. Particle Swarm optimization algorithm technique is developed by inspiring flock animals such as birds, fishes and insects. Each animal in the flock (particle) arranges its position according to other particles in the flock and its previous position. This process continues until reaching the target. Big Bang – Big Crunch optimization algorithm is inspired by the big bang and big crunch hypothesis of the universe. A unique point which is called as Center point or center of mass is generated in the Big Bang phase. In the scope of the algorithm, new candidates are determined around the center of mass, suitability of them is calculated and the best one among them is selected. In this study, optimum sizing of three steel frames (three storey- three bay,five storey- three bay,seven storey- three bay rigid steel frames) by considering minimum weight values is performed by using these algorithms. Designs are carried out in accordance with the principles in LFRD-AISC (Load and Resistance Factor Design - American Institute of Steel Construction). Besides, three techniques are compared to each other, the particle swarm algorithm showed better performance than the others in terms of finding the best results in twoexamples, hunting search algorithm also followed it. The results of the technique in which the least weight is calculated is taken into consideration and the displacement analysis of the frames are made under loading by SAP2000 analysis program. Finally, peak point lateral displacements are determined and interpretations are made.

References

  • Arsan, T., 2018, “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”, Journal of Natural and Applied Sciences, Cilt 22, ss. 367-374.
  • Aydoğdu, İ., Akın, A., Saka, M. P., 2016, “Design optimization of real World steel space frames using artificial bee colony algorithm with Levy flight distribution”, Advances in Engineering Software, Volume 92, pp. 1-14.
  • Doğan, E., 2010, “Optimum Design Of Rigid and Semi-Rigid Steel Sway Frames Including Soil- Structure Interaction”, Ph.D. Dissertation, Middle East Technical University, Ankara, Turkey, 262 p.
  • Doğan, E. Saka, M. P., 2012, “Optimum design of unbraced steel frames to the LRFD-AISC code using particle swarm optimization”, Advances in Engineering Software,Volume 46, pp. 27–34.
  • Erdem, R. T., 2015, “Non-linear performance analysis of existing and concentric braced steel structures”, Steel and Composite Structures, Volume19, Issue 1,pp. 59-74.
  • Erol, O. K.,Eksin, I. A.,2006,“A new optimization method: Big Bang – Big Crunch”, Advances in Engineering Software, Volume 37, pp. 106–11.
  • Gücüyen, E., Erdem, R. T., 2014, “Corrosion effects on structural behaviour of jacket type offshore structures”, Gradevinar, Volume 66, Issue 11, pp. 981-986.
  • Kamal, O. El-Mahdy O., Nour, M. El-Komy, G., 2015,“Optimization of Steel Structures Using Particle Swarm Technique”, The Engineering and Scientific Research Journal,pp.1-14.
  • Khalilporazari, S., Khalilporazari, Pasandideh, S. H. R., “Modeling and optimization of multi-item multi-constrained EOQ model for growing items”, Knowledge-Based Systems,Volume 164, pp. 150-162.
  • Khalilporazari, S., Khalilporazari, S., “A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization ofthe surface grinding process”, Engineering Optimization, Volume 49, pp. 878-895.
  • Kaveh, A. Abbasgholiha, H., 2011,“Optimum design of steel sway frames using Big Bang-Big Crunch algorithm”, Asian Journal of Civil Engineering,Volume 12, pp. 293-317.
  • Kaveh, A., Bakhshpoori, T.,2013,“Optimum design of steel frames using Cuckoo Search algorithm with Levy flights”, Struct. Design Tall Spec. Build. Volume22, pp. 1023–1036.
  • Kennedy, J., Eberhart, R., 1995, “Particle swarm optimization”,Proceedings of International Conference Neural Networks, Volume 4, pp. 1942-1948.
  • LRFD-AISC Manual of Steel Construction, 2011,“Load and Resistance Factor Design”, 3rd Edition. American Institute of Steel Construction, USA.
  • Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.,2010,“A novel meta-heuristic optimizationalgorithm inspired by group hunting of animals: Hunting search.” ComputersMathematics with Applications, Vol. 60, pp. 2087-2098.
  • Reynolds, C., W., 1987, “Flocks, herds, and schools: A distributed behavioral model”, ACM Computer Graphics, Volume21, pp. 25–34.
  • Saka, M. P., 2009,“Optimum design of steel sway frames to BS5950 using harmony search algorithm”, Journal of Constructional Steel Research, Volume 65, pp. 36-43.
  • Saka, M. P.,Doğan, E.,Aydoğdu,İ.,2013,“Review and Analysis of Swarm-Intelligence Based Algorithms. Swarm Intelligence and Bio-Inspired Computation”, Theory and Applications, Chapter: 2. Ed: X-S Yang, Z Cui, R. Xiao, A. M. Gandomi, M. Karamanoğlu, Elsevier.
  • SAP 2000 Analysis Reference Manual. Computers and Structures Inc., Berkeley.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Aybike Özyüksel Çiftçioğlu

Erkan Doğan

Publication Date December 4, 2019
Submission Date February 27, 2019
Acceptance Date May 30, 2019
Published in Issue Year 2019 Volume: 7 Issue: 4

Cite

IEEE A. Özyüksel Çiftçioğlu and E. Doğan, “ÇELİK ÇERÇEVELERİN FARKLI STOKASTİK YÖNTEMLER KULLANILARAK OPTİMUM BOYUTLANDIRILMASI”, KONJES, vol. 7, no. 4, pp. 847–861, 2019, doi: 10.36306/konjes.654958.