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TAM DERİNLİKLİ ESNEK ÜSTYAPILARIN KATMAN ÖZELLİKLERİNİN TAHMİNİ İÇİN LİG ŞAMPİYONASI ALGORİTMASI

Yıl 2020, Cilt: 8 Sayı: 1, 273 - 284, 20.03.2020

Onur Pekcan

https://doi.org/10.21923/jesd.693743

Öz

Bu çalışma tam derinlikli esnek üstyapıların katman özelliklerinin tahmin edilmesinde kullanılacak, Lig Şampiyonası Algoritması (LCA) ve Yapay Sinir Ağları (ANN) tabanlı bir geri hesaplama algoritması önermektedir. LCA-ANN adı verilen bu algoritma iki ana bölümden oluşmaktadır: (i) yol yüzeyindeki deplasmanların hesaplandığı, lineer olmayan sonlu elemanlar çözümleri ile geliştirilen, ANN ileri hesaplama modeli ve (ii) ANN modeline girdi olarak verilecek en uygun katman elastisite modullerinin belirlenmesinde kullanınan LCA arama algoritmasıdır. Önerilen yönetimin performansını değerlendirmek amacıyla sentetik olarak üretilen veri seti ile gerçek bir veri seti kullanılmıştır. Ayrıca, LCA’nın arama yeteğini değerlendirmek için, kabul görmüş algoritmalar olan Basit Genetik Algoritma (SGA) ve Parçacık Sürü Optimizasyonu (PSO) karşılaştırma amacıyla kullanılmıştır. Elde edilen sonuçlar göstermiştir ki önerilen algoritma düşük hata miktarlarıyla esnek üstyapı katman özellikleri tahmin edebilmekte ve geri hesaplama çalışmalarında hızlı ve güvenilir bir yöntem olarak ortaya çıkmaktadır. 

Anahtar Kelimeler

Esnek Yol Kaplaması Geri Hesaplama Lig Şampiyonası Algoritması Yapay Sinir Ağları

Kaynakça

  • Baltacıoğlu, A.K., Civalek, Ö., Akgöz, B., Korkmaz, A., 2010. Deprem Hasarlarinin Hizli Tespitinde Yapay Sinir Ağlari Yaklaşimi Artificial Neural Networks Approach for Fast Earthquake Damage Determination. Mühendislik Bilimleri ve Tasarım Dergisi. 1 (1), 22–27.
  • Ceylan, H., Guclu, A., Tutumluer, E., Thompson, M.R., 2005. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering. 6 (3), 171–182.
  • FHWA, n.d. LTPP InfoPave [Web Document]. URL https://infopave.fhwa.dot.gov/ (accessed 2.18.20).
  • Fileccia Scimemi, G., Turetta, T., Celauro, C., 2016. Backcalculation of airport pavement moduli and thickness using the Levy Ant Colony Optimization Algorithm. Construction and Building Materials. 119 (2016), 288–295.
  • Goktepe, A.B., Agar, E., Lav, A.H., 2006. Advances in backcalculating the mechanical properties of flexible pavements. Advances in Engineering Software. 37 (7), 421–431.
  • Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc.
  • Gopalakrishnan, K., 2009. Backcalculation of Pavement Moduli Using Bio-Inspired Hybrid Metaheuristics and Cooperative Strategies. Proceedings of the 2009 Mid-Continent Transportation Research Symposium. Ames, IA.
  • Gopalakrishnan, K., Khaitan, S.K., 2010. Development of an intelligent pavement analysis toolbox. Proceedings of the ICE - Transport. 163 (4), 211–221.
  • Gurney, K., 2005. An introduction to neural networks. Taylor & Francis.
  • Hu, K.-F., Jiang, K.-P., Chang, D.-W., 2007. Study of Dynamic Backcalculation Program with Genetic Algorithms for FWD on Pavements. Tamkang Journal of Science and Engineering. 10 (4), 297–305.
  • Huang, Y., 2003. Pavement Analysis and Design, 2nd ed. New Jersey (NJ): Pearson Prentice Hall.
  • Kashan, A.H., 2009. League Championship Algorithm: A New Algorithm for Numerical Function Optimization. 2009 International Conference of Soft Computing and Pattern Recognition. IEEE, pp. 43–48.
  • Kashan, A.H., 2014. League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships. Applied Soft Computing. 16, 171–200.
  • Katanalp, B.Y., Yıldırım, Z.B., Karacasu, M., İbrikçi, T., 2019. Betonu Performans Karakteristiklerinin Yapay Sinir Ağları ve Merkezi Kompozit Tasarım Yöntemleri Kullanılarak Karşılaştırılması. Mühendislik Bilimleri ve Tasarım Dergisi. 7 (3), 680–688.
  • Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. pp. 1942–1948.
  • Keskin, M.E., Taylan, E.D., 2010. Artificial Intelligent Models For Flow Prediction : A Case Study On Alara Stream. Journal of Engineering Science and Design. 1 (1), 8–13.
  • Kim, N., Im, S.-B., 2005. A comparative study on measured vs. Predicted pavement responses from falling weight deflectometer (FWD) measurements. KSCE Journal of Civil Engineering. 9 (2), 91–96.
  • Li, M., Wang, H., 2019. Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. International Journal of Pavement Engineering. 20 (4), 490–498.
  • Meier, R., Rix, G., 1994. Backcalculation of flexible pavement moduli using artificial neural networks. Transportation Research Record. 1448 , 75–82.
  • Meier, R.W., 1995. Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks. U.S. Army Engineer Waterways Ecperimental Station.
  • Öcal, A., 2014. Backcalculation of Pavement Layer Properties Using Artificial Neural Network Based Gravitational Search Algorithm. M.Sc. Thesis, Middle East Technical University, Ankara, Turkey.
  • Rakesh, N., Jain, A.., Reddy, M.A., Reddy, K.S., 2006. Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduli. International Journal of Pavement Engineering. 7 (3), 221–230.
  • Reddy, M.A., Reddy, K.S., Pandey, B.B., 2004. Selection of Genetic Algorithm Parameters for Backcalculation of Pavement Moduli. International Journal of Pavement Engineering. 5 (2), 81–90.
  • Saltan, M., Tigdemir, M., Karasahin, M., 2002. Artificial neural network application for flexible pavement thickness modeling. Turkish Journal of Engineering and Environmental Sciences. 26, 243–248.
  • Saltan, M., Uz, V.E., Aktas, B., 2013. Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement. Neural Computing and Applications. 23 (6), 1703–1710.
  • Sangghaleh, A., Pan, E., Green, R., Wang, R., Liu, X., Cai, Y., 2014. Backcalculation of pavement layer elastic modulus and thickness with measurement errors. International Journal of Pavement Engineering. 15 (6), 521–531.
  • Scullion, T., Uzan, J., Paredes, M., 1990. Modulus: A Microcomputer-Based Backcalculation System. Transportation Research Record. (1260), 180–191.
  • Sharma, S., Das, A., 2008. Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Canadian Journal of Civil Engineering. 35 (1), 57–66.
  • Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
  • Sivaneswaran, N., Kramer, S.L., Mahoney, J.P., 1991. Advanced Backcalculation Using a Nonlinear Least Squares Optimization Technique. Transportation Research Record. (1293), 93–102.
  • Thompson, M.R., Robnett, Q.L., 1979. Resilient Properites of Subgrade Soils. Journal of Transportation Engineering, ASCE. 105 (1), 71–89.
  • Uzan, J., Scullion, R., Michalek, C., Parades, M., Lytton, R., 1988. A microcomputer based procedure for backcalculating layer moduli from FWD data, Research Report 1123 -1.
  • Washington State Department of Transportation, 2005. EVERSERIES USER’S GUIDE Pavement Analysis Computer Software and Case Studies.
  • Zhou, H., Hicks, R.G., Bell, C.A., 1990. Bousdef: A Backcalculation Program for Determining Moduli of a Pavement Structure. Transportation Research Record. 1260 , 166–179.

LEAGUE CHAMPIONSHIP ALGORITHM FOR LAYER MODULI ESTIMATION OF FULL-DEPTH ASPHALT PAVEMENTS

Yıl 2020, Cilt: 8 Sayı: 1, 273 - 284, 20.03.2020

Onur Pekcan

https://doi.org/10.21923/jesd.693743

Öz

This study proposes a backcalculation tool, based on the hybrid use of League Championship Algorithm (LCA) and Artificial Neural Network (ANN), in order to predict the stiffness related layer properties of full-depth asphalt pavements. The proposed algorithm, namely LCA-ANN, is composed of two main parts; (i) an ANN forward response model, which is developed with the nonlinear finite element solution, for computing the surface deflections, and (ii) LCA search algorithm which is employed to search and provide the best set of layer moduli to the ANN model. In order to evaluate the performance of the proposed method, a synthetically generated dataset and real field data are utilized. Moreover, to assess the searching ability of LCA, well-accepted metaheuristic algorithms; Simple Genetic Algorithm (SGA) and Particle Swarm Optimization (PSO) are employed for comparison purposes. Obtained results reveal that the proposed algorithm can predict the layer properties with a low order of error values and enables fast and reliable tool for backcalculation studies.

Anahtar Kelimeler

Flexible Pavements Backcalculation League Championship Algorithm Artificial Neural Network

Kaynakça

  • Baltacıoğlu, A.K., Civalek, Ö., Akgöz, B., Korkmaz, A., 2010. Deprem Hasarlarinin Hizli Tespitinde Yapay Sinir Ağlari Yaklaşimi Artificial Neural Networks Approach for Fast Earthquake Damage Determination. Mühendislik Bilimleri ve Tasarım Dergisi. 1 (1), 22–27.
  • Ceylan, H., Guclu, A., Tutumluer, E., Thompson, M.R., 2005. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering. 6 (3), 171–182.
  • FHWA, n.d. LTPP InfoPave [Web Document]. URL https://infopave.fhwa.dot.gov/ (accessed 2.18.20).
  • Fileccia Scimemi, G., Turetta, T., Celauro, C., 2016. Backcalculation of airport pavement moduli and thickness using the Levy Ant Colony Optimization Algorithm. Construction and Building Materials. 119 (2016), 288–295.
  • Goktepe, A.B., Agar, E., Lav, A.H., 2006. Advances in backcalculating the mechanical properties of flexible pavements. Advances in Engineering Software. 37 (7), 421–431.
  • Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc.
  • Gopalakrishnan, K., 2009. Backcalculation of Pavement Moduli Using Bio-Inspired Hybrid Metaheuristics and Cooperative Strategies. Proceedings of the 2009 Mid-Continent Transportation Research Symposium. Ames, IA.
  • Gopalakrishnan, K., Khaitan, S.K., 2010. Development of an intelligent pavement analysis toolbox. Proceedings of the ICE - Transport. 163 (4), 211–221.
  • Gurney, K., 2005. An introduction to neural networks. Taylor & Francis.
  • Hu, K.-F., Jiang, K.-P., Chang, D.-W., 2007. Study of Dynamic Backcalculation Program with Genetic Algorithms for FWD on Pavements. Tamkang Journal of Science and Engineering. 10 (4), 297–305.
  • Huang, Y., 2003. Pavement Analysis and Design, 2nd ed. New Jersey (NJ): Pearson Prentice Hall.
  • Kashan, A.H., 2009. League Championship Algorithm: A New Algorithm for Numerical Function Optimization. 2009 International Conference of Soft Computing and Pattern Recognition. IEEE, pp. 43–48.
  • Kashan, A.H., 2014. League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships. Applied Soft Computing. 16, 171–200.
  • Katanalp, B.Y., Yıldırım, Z.B., Karacasu, M., İbrikçi, T., 2019. Betonu Performans Karakteristiklerinin Yapay Sinir Ağları ve Merkezi Kompozit Tasarım Yöntemleri Kullanılarak Karşılaştırılması. Mühendislik Bilimleri ve Tasarım Dergisi. 7 (3), 680–688.
  • Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. pp. 1942–1948.
  • Keskin, M.E., Taylan, E.D., 2010. Artificial Intelligent Models For Flow Prediction : A Case Study On Alara Stream. Journal of Engineering Science and Design. 1 (1), 8–13.
  • Kim, N., Im, S.-B., 2005. A comparative study on measured vs. Predicted pavement responses from falling weight deflectometer (FWD) measurements. KSCE Journal of Civil Engineering. 9 (2), 91–96.
  • Li, M., Wang, H., 2019. Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. International Journal of Pavement Engineering. 20 (4), 490–498.
  • Meier, R., Rix, G., 1994. Backcalculation of flexible pavement moduli using artificial neural networks. Transportation Research Record. 1448 , 75–82.
  • Meier, R.W., 1995. Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks. U.S. Army Engineer Waterways Ecperimental Station.
  • Öcal, A., 2014. Backcalculation of Pavement Layer Properties Using Artificial Neural Network Based Gravitational Search Algorithm. M.Sc. Thesis, Middle East Technical University, Ankara, Turkey.
  • Rakesh, N., Jain, A.., Reddy, M.A., Reddy, K.S., 2006. Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduli. International Journal of Pavement Engineering. 7 (3), 221–230.
  • Reddy, M.A., Reddy, K.S., Pandey, B.B., 2004. Selection of Genetic Algorithm Parameters for Backcalculation of Pavement Moduli. International Journal of Pavement Engineering. 5 (2), 81–90.
  • Saltan, M., Tigdemir, M., Karasahin, M., 2002. Artificial neural network application for flexible pavement thickness modeling. Turkish Journal of Engineering and Environmental Sciences. 26, 243–248.
  • Saltan, M., Uz, V.E., Aktas, B., 2013. Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement. Neural Computing and Applications. 23 (6), 1703–1710.
  • Sangghaleh, A., Pan, E., Green, R., Wang, R., Liu, X., Cai, Y., 2014. Backcalculation of pavement layer elastic modulus and thickness with measurement errors. International Journal of Pavement Engineering. 15 (6), 521–531.
  • Scullion, T., Uzan, J., Paredes, M., 1990. Modulus: A Microcomputer-Based Backcalculation System. Transportation Research Record. (1260), 180–191.
  • Sharma, S., Das, A., 2008. Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Canadian Journal of Civil Engineering. 35 (1), 57–66.
  • Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
  • Sivaneswaran, N., Kramer, S.L., Mahoney, J.P., 1991. Advanced Backcalculation Using a Nonlinear Least Squares Optimization Technique. Transportation Research Record. (1293), 93–102.
  • Thompson, M.R., Robnett, Q.L., 1979. Resilient Properites of Subgrade Soils. Journal of Transportation Engineering, ASCE. 105 (1), 71–89.
  • Uzan, J., Scullion, R., Michalek, C., Parades, M., Lytton, R., 1988. A microcomputer based procedure for backcalculating layer moduli from FWD data, Research Report 1123 -1.
  • Washington State Department of Transportation, 2005. EVERSERIES USER’S GUIDE Pavement Analysis Computer Software and Case Studies.
  • Zhou, H., Hicks, R.G., Bell, C.A., 1990. Bousdef: A Backcalculation Program for Determining Moduli of a Pavement Structure. Transportation Research Record. 1260 , 166–179.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği
Bölüm Araştırma Makalesi \ Research Makaleler
Yazarlar

Onur Pekcan 0000-0003-3603-5929

Yayımlanma Tarihi 20 Mart 2020
Gönderilme Tarihi 24 Şubat 2020
Kabul Tarihi 16 Mart 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Pekcan, O. (2020). LEAGUE CHAMPIONSHIP ALGORITHM FOR LAYER MODULI ESTIMATION OF FULL-DEPTH ASPHALT PAVEMENTS. Mühendislik Bilimleri Ve Tasarım Dergisi, 8(1), 273-284. https://doi.org/10.21923/jesd.693743
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