Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2018 / Cilt: 24 - Sayı: 6

Cost optimum design of spread footing under uniaxial combined bending according to TS500 via various metaheuristic algorithms

Bir doğrultuda birleşik eğilme etkisindeki tekil temellerin çeşitli sezgisel algoritmalarla TS500’e göre minimum maliyetle optimum tasarımı

Pages
1030–1036
Publication Date
2018-12-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been observed that the minimum cost of spread footing has decreased with the increase of the bearing capacity of soil.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, optimum designs of reinforced concrete spread footings bases with minimum cost were realized with various metaheuristic algorithms known as Artificial Bee Colony, Cuckoo Search, Teaching-Learning-Based Optimization Algorithm.
📊
Key Findings & Data:The findings obtained from the optimum designs performed by the algorithms are evaluated according to various performance criteria.
💡
Conclusion & Impact:It has been observed that the minimum cost of spread footing has decreased with the increase of the bearing capacity of soil.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tekil temel · Betonarme · Optimizasyon

Abstract

In this study, optimum designs of reinforced concrete spread footings bases with minimum cost were realized with various metaheuristic algorithms known as Artificial Bee Colony, Cuckoo Search, Teaching-Learning-Based Optimization Algorithm. The findings obtained from the optimum designs performed by the algorithms are evaluated according to various performance criteria. Teaching-Learning-Based optimization algorithm showed the highest performance and convergence speed. On the other hand, the effect of bending moment-axial force level, bearing capacity of soil on the optimum design of the spread footing are investigated by some parametric studies. It is understood that the increase in axial force increases the minimum cost of the spread footing and the increase in the eccentricity level rises the degree of increase. It has been observed that the minimum cost of spread footing has decreased with the increase of the bearing capacity of soil.

Özet

Bu çalışmada betonarme tekil temellerin minimum maliyetle optimum tasarımları Yapay Arı Koloni, Guguk Kuşu Arama, Öğretme-Öğrenme Tabanlı Optimizasyon Algoritması adlarıyla bilinen çeşitli sezgisel algoritmalarla gerçekleştirilmiştir. Söz konusu algoritmalarla gerçekleştirilen optimum tasarımlardan elde edilen bulgular çeşitli başarım (performans) ölçütlerine göre değerlendirilmiştir. Algoritmalardan en yüksek başarımı ve en yüksek yakınsama hızını öğretme ve öğrenme tabanlı optimizasyon algoritması göstermiştir. Diğer taraftan temelin eğilme momenti ve normal kuvvet düzeyi ile temel zeminin taşıma gücünün optimum tasarıma etkisi birtakım parametrik çalışmalarla incelenmiştir. Gerçekleştirilen parametrik çalışmalardan normal kuvvetin artmasıyla tekil temelin minimum maliyetinin arttığı, ayrıca dış merkezlik düzeyinin artmasıyla bu artış düzeyinin de yükseldiği anlaşılmıştır. Zemin emniyet gerilmesinin artmasıyla temelin minimum maliyetinin azaldığı görülmüştür.

🔖

Cite This Article

ÖZTÜRK, H. T. (2018). Cost optimum design of spread footing under uniaxial combined bending according to TS500 via various metaheuristic algorithms. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 24 - Sayı: 6, 1030–1036. https://ajindex.com/articles/42275

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Öztürk HT, Durmuş A. "Optimum cost design of RC columns using artificial bee colony algorithm". Structural Engineering and Mechanics, 45(5), 643-654, 2013.
  2. Öztürk HT, Durmuş A, Durmuş A. "Optimum design of a reinforced concrete beam using artificial bee colony algorithm". Computers and Concrete, 10(3), 295-306, 2012.
  3. Govindaraj V, Ramasamy JV. “Optimum detailed design of reinforced concrete continuous beams using genetic algorithms”. Computers & Structures, 84(1), 34-48, 2005.
  4. Sahab MG, Ashour AF, Toropov VV. “Cost optimisation of reinforced concrete flat slab buildings”. Engineering Structures, 27(3), 313-322, 2005.
  5. Öztürk HT. "Optimum design of rc cantilever retaining wall using artificial bee colony and cuckoo search algorithms". 12th International Congress on Advances in Civil Engineering, Istanbul, Turkey, 21-23 September 2016.
  6. Paya-Zaforteza I, Yepes V, Hospitaler A, Gonzalez-Vidosa, F. “CO2-optimization of reinforced concrete frames by simulated annealing”. Engineering Structures, 31(7), 1501-1508, 2009.
  7. Öztürk HT, Türkeli E, Durmuş A. "Optimum design of RC shallow tunnels in earthquake zones using artificial bee colony and genetic algorithms". Computers and Concrete, 17(4), 435-453, 2016.
  8. Khajehzadeh M, Taha MR, Eslami M. “Modified particle swarm optimization for optimum design of spread footing and retaining wall”. Journal of Zhejiang University-Science A, 12(6), 415-427,2011.
  9. Khajehzadeh M, Taha MR, Eslami M. “A new hybrid firefly algorithm for foundation optimization”. National Academy Science Letters, 36(3), 279-288, 2013.
  10. Camp CV, Assadollahi A. “CO2 and cost optimization of reinforced concrete footings using a hybrid big bang-big crunch algorithm”. Structural and Multidisciplinary Optimization, 48(2), 411-426, 2013.
  11. Camp CV, Assadollahi A. “CO2 and cost optimization of reinforced concrete footings subjected to uniaxial uplift”. Journal of Building Engineering, 3, 171-183, 2015.
  12. Karaboga D, Basturk B. “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”. Journal of Global Optimization, 39(3), 459-471, 2007.
  13. Teodorovic D, Dell’Orco M. “Bee colony optimization–a cooperative learning approach to complex transportation problems”. Advanced OR and AI Methods in Transportation, 51-60, 2005.
  14. Yang XS. “Engineering optimizations via nature-inspired virtual bee algorithms”. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, 317-323, 2005.
  15. Pham DT, Haj Darwish