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Beton Basınç Dayanımının JAYA ve Öğretme-Öğrenme Tabanlı Optimizasyon (TLBO) Algoritmalarıyla Modellenmesi

Year 2018, Volume: 1 Issue: 2, 24 - 29, 30.12.2018

Abstract

Bu çalışmada, laboratuvarda üretilen küp numunelerin, basınç dayanımları belirlenmiş, ultrasonik dalga iletim hızı ölçümleri, Schmidt çekiciyle sıçrama sayısı ölçümleri yapılmış ve tartımla boşluk oranları belirlenmiştir. Ultrason testinden elde edilen dalga iletim hızı, Schmidt çekiciyle belirlenen sıçrama sayısı ve tartım yoluyla hesaplanan boşluk oranı arasında bir regresyon ilişkisi kurarak bu ilişki yardımıyla beton dayanımını tahmin etmek amaçlanmaktadır. Deneylerden elde edilen verilerin regresyon fonksiyonlarına Öğretme-öğrenme tabanlı optimizasyon algoritması (TLBO) ve JAYA algoritmaları uygulanmıştır. Giriş parametreleri, ultrason ölçümleri, Schmidt çekiciyle elde edilen sıçrama sayısı ve tartımla hesaplanan boşluk oranı sonucu elde edilen ortalama dalga iletim hızıdır. Öğretme-öğrenme tabanlı optimizasyon algoritması (TLBO) ve JAYA algoritmalarının karşılaştırılması için, algoritmalar; karesel, üstel, doğrusal, S fonksiyonu, Ters, Ln fonksiyonu ve üs adlarıyla anılan yedi farklı regresyon formuna uygulanmıştır. Modellerin başarımını değerlendirmek için; ortalama karesel hata, ortalama karesel hatanın karekökü, ortalama mutlak hata, ortalama mutlak hata oranı ve belirleme katsayısı gibi beş istatistiksel endeks kullanılmıştır.

References

  • Ni, H. G., & Wang, J. Z. (2000). Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 30(8), 1245-1250.
  • Topcu, I. B., & Sarıdemir, M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41(3), 305-311.
  • Yuan, Z., Wang, L. N., & Ji, X. (2014). Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS. Advances in Engineering Software, 67, 156-163.
  • Nikoo, M., Torabian Moghadam, F., & Sadowski, Ł. (2015). Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015.
  • Yu, Y., Li, W., Li, J., & Nguyen, T. N. (2018). A novel optimised self-learning method for compressive strength prediction of high performance concrete. Construction and Building Materials, 184, 229-247.
  • Uzlu, E., Kömürcü, M. İ., Kankal, M., Dede, T., & Öztürk, H. T. (2014). Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Applied Ocean Research, 48, 103-113.
  • Öztürk, N., Şentürk, H.B., Gündoğdu, A., & Duran, C., (2014). Cd(II)’nin İçme Suyu Arıtma Tesisi Atık Çamuru Üzerine Adsorpsiyonu: Modelleme ve Optimizasyon, 7. Ulusal Analitik Kimya Kongresi Bildiriler Kitabı, 234-234.
  • Bayram, A., Uzlu, E., Kankal, M., & Dede, T. (2015). Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm. Environmental Earth Sciences, 73(10), 6565-6576.
  • Anılan, T., Uzlu, E., Kankal, M., & Yuksek, O. (2018). The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms. Scientia Iranica, 25(2), 632-645.
  • Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1), 1-15.

Modelling Compressive Strength of Concrete via Teaching–Learning Based Optimization and JAYA Algorithms

Year 2018, Volume: 1 Issue: 2, 24 - 29, 30.12.2018

Abstract

In
this study, compressive strength tests, ultrasonic wave transmission speed
measurements, Schmidt rebound test hammer measurements were made on the cube
samples and void ratios were determined by weighing. It is aimed to estimate
the concrete strength with these measurements by establishing a regression
relation between the wave transmission speed obtained from the ultrasound test,
rebound values from Schmidt rebound hammer and void ratio calculated by
weighting. Teaching–learning-based optimization (TLBO) and JAYA algorithms were
applied to regression functions of the data from the tests. The input
parameters are the average wave transmission speed obtained as a result of
ultrasound measurements, rebound values from Schmidt rebound hammer and void
ratio calculated by weighting. The accuracy of TLBO method is compared with
those of the JAYA algorithm. These methods are applied to seven different
regression forms: quadratic, exponential, linear, S function, Inverse, Ln
function and power. To evaluate the performance of the models, five statistical
indices, i.e., sum square error, root mean square error, mean absolute error,
average relative error, and determination coefficient, are used.

References

  • Ni, H. G., & Wang, J. Z. (2000). Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 30(8), 1245-1250.
  • Topcu, I. B., & Sarıdemir, M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41(3), 305-311.
  • Yuan, Z., Wang, L. N., & Ji, X. (2014). Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS. Advances in Engineering Software, 67, 156-163.
  • Nikoo, M., Torabian Moghadam, F., & Sadowski, Ł. (2015). Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015.
  • Yu, Y., Li, W., Li, J., & Nguyen, T. N. (2018). A novel optimised self-learning method for compressive strength prediction of high performance concrete. Construction and Building Materials, 184, 229-247.
  • Uzlu, E., Kömürcü, M. İ., Kankal, M., Dede, T., & Öztürk, H. T. (2014). Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Applied Ocean Research, 48, 103-113.
  • Öztürk, N., Şentürk, H.B., Gündoğdu, A., & Duran, C., (2014). Cd(II)’nin İçme Suyu Arıtma Tesisi Atık Çamuru Üzerine Adsorpsiyonu: Modelleme ve Optimizasyon, 7. Ulusal Analitik Kimya Kongresi Bildiriler Kitabı, 234-234.
  • Bayram, A., Uzlu, E., Kankal, M., & Dede, T. (2015). Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm. Environmental Earth Sciences, 73(10), 6565-6576.
  • Anılan, T., Uzlu, E., Kankal, M., & Yuksek, O. (2018). The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms. Scientia Iranica, 25(2), 632-645.
  • Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1), 1-15.
There are 12 citations in total.

Details

Primary Language Turkish
Journal Section Research Papers
Authors

Hasan Tahsin Öztürk 0000-0001-8479-9451

Publication Date December 30, 2018
Submission Date December 30, 2018
Acceptance Date December 31, 2018
Published in Issue Year 2018 Volume: 1 Issue: 2

Cite

APA Öztürk, H. T. (2018). Beton Basınç Dayanımının JAYA ve Öğretme-Öğrenme Tabanlı Optimizasyon (TLBO) Algoritmalarıyla Modellenmesi. Journal of Investigations on Engineering and Technology, 1(2), 24-29.