Obtaining Live Load Distribution Factors Equations for Simply Supported Bridges Using Neural Networks

Öz

Advancements in artificial intelligence have caused important transformations in many areas. Research on applications of artificial intelligence, machine-learning and neural networks in civil engineering has been growing recently. Parallel to this progress, in this study, closed-form formulas for distribution of live load among the bridge girders are obtained using artificial neural networks. In these formulas, the number of girders is also incorporated as a new parameter, which is not included in AASHTO LRFD live load distribution equations. For this purpose, numerous straight, simply supported bridge models are analyzed using the finite element method and subsequently live load distribution factors are calculated. Live load distribution factors obtained through neural networks are compared with those from finite element analyses and AASHTO LRFD formulas. These comparisons reveal that closed-form formulas can predict live load distribution factors accurately.

Anahtar Kelimeler

• Artificial intelligence
• Neural networks
• Bridges
• Live load
• Load distribution

Basit Mesnetli Köprülerde Hareketli Yük Dağılım Faktörleri Denklemlerinin Yapay Sinir Ağları ile Elde Edilmesi

Öz

Yapay zekâ konusunda kaydedilen ilerlemeler günümüzde her alanda çok önemli dönüşümlere neden olmaktadır. İnşaat mühendisliği alanında da yapay zekâ, makine öğrenmesi ve yapay sinir ağları uygulamaları ve kullanımı her geçen gün artmakta ve çeşitlenmektedir. Bu gelişmelere paralel olarak, bu çalışmada, yapay sinir ağları kullanılarak köprü tasarımında kullanılan hareketli yüklerin köprü kirişlerine dağılımı için kapalı formüller elde edilmiştir. Bu formüllerde, farklı yapısal köprü parametrelerinin yanı sıra, AASHTO LRFD’de verilen denklemlerde dahil edilmemiş olan kiriş sayısı parametresi de eklenmiştir. Bu amaçla, birçok verevsiz basit mesnetli köprü modeli hazırlanarak olası tüm kamyon yükleri altında sonlu elemanlar analizleri yapılmış ve hareketli yük dağılım katsayıları elde edilmiştir. Yapay sinir ağları ile elde edilen hareketli yük dağılım faktörleri, sonlu elemanlar analiz sonuçları ile ve AASHTO LRFD’de verilmiş olan hareketli yük dağılım katsayıları ile karşılaştırılmıştır. Bu karşılaştırmalar göstermektedir ki, sinir ağları ile elde edilen formüller dağılım faktörlerini oldukça iyi tahmin edebilmektedir.

Anahtar Kelimeler

• Yapay zekâ
• Yapay sinir ağları
• Köprüler
• Hareketli yük
• Dağılım faktörleri

Kaynakça

1. 1. LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep Learning. Nature, 521(7553), 436-444.
2. 2. Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Jain, A., 2019. Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature. Nature, 571(7763), 95-98.
3. 3. Moayedi, H., Mosallanezhad, M., Rashid, A.S.A., Jusoh, W.A.W., Muazu, M.A., 2018. A Systematic Review and Meta-Analysis of Artificial Neural Network Application in Geotechnical Engineering: Theory and Applications. Neural Computing and Applications, 1-24.
4. 4. Shahin, M.A., Jaksa, M.B., Maier, H.R., 2001. Artificial Neural Network Applications in Geotechnical Engineering. Australian Geomechanics, 36(1), 49-62.
5. 5. Salehi, H., Burgueno, R., 2018. Emerging Artificial Intelligence Methods in Structural Engineering. Engineering Structures, 171, 170-189.
6. 6. Ng C-T., 2014. On the Selection of Advanced Signal Processing Techniques for Guided Wave Damage Identification Using a Statistical Approach. Engineering Structures, 67, 50–60.
7. 7. Akbas, B., Doran, B., Alacali, S., Akşar, B., 2016. Estimating Stiffness Modification Factor for the Coupling Beam of Coupled Shear Walls Using a Neural Network Model. Karaelmas Science & Engineering Journal, 6(2), 273-282.
8. 8. Chen, G., Li, T., Chen, Q., Ren, S., Wang, C., Li, S., 2019. Application of Deep Learning Neural Network to Identify Collision Load Conditions Based on Permanent Plastic Deformation of Shell Structures. Computational Mechanics, 64(2), 435-449.

9. 9. Mazanoğlu, K., Kandemir-Mazanoğlu, E.Ç., 2017. Çatlaklı Kirişlerin Yapay Sinir Ağları ile Modellenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(3), 1129-1135.
10. 10. Mansour, M.Y., Dicleli, M., Lee, J.Y., Zhang, J., 2004. Predicting the Shear Strength of Reinforced Concrete Beams Using Artificial Neural Networks. Engineering Structures, 26(6), 781-799.
11. 11. Falcone, R., Lima, C., Martinelli, E., 2020. Soft Computing Techniques in Structural and Earthquake Engineering: a Literature Review. Engineering Structures, 207, 110269.
12. 12. Xiao, F., Fan, J., Chen, G.S., Hulsey, J.L., 2019. Bridge Health Monitoring and Damage Identification of Truss Bridge Using Strain Measurements. Advances in Mechanical Engineering, 11(3), 1687814019832216.
13. 13. Salomon, A.L., Wells, J., 2018. Exploiting Imagery Data Collected with Unmanned Aircraft Systems (UAS) for Bridge Inspections (No. 18-03134).
14. 14. Gupta, R.K., Kumar, S., Patel, K.A., Chaudhary, S., Nagpal, A.K., 2015. Rapid Prediction of Deflections in Multi-span Continuous Composite Bridges Using Neural Networks, International Journal of Steel Structures, 15(4), 893-909.
15. 15. Fahmy, A.S., El-Madawy, M. E. T., Gobran, Y. A., 2016. Using Artificial Neural Networks in the Design of Orthotropic Bridge Decks. Alexandria Engineering Journal, 55(4), 3195-3203.
16. 16. Xu, G., Chen, Q., Chen, J., 2018. Prediction of Solitary Wave Forces on Coastal Bridge Decks Using Artificial Neural Networks. Journal of Bridge Engineering, 23(5), 04018023.
17. 17. AASHTO LRFD, 2017. AASHTO LRFD Bridge Design Specifications. 8th ed. Washington, DC: American Association of State Highway and Transportation Officials.
18. 18. Zokaie, T., Osterkamp, T.A., Imbsen, R.A., 1991. Distribution of Wheel Loads on Highway Bridges, NCHRP 12-26 Final Rep., National Cooperative Highway Research Program, Washington, D.C.
19. 19. Yalcin, O.F., Dicleli, M., 2013. Comparative Study on the Effect of Number of Girders on Live Load Distribution in Integral Abutment and Simply Supported Bridge Girders. Advances in Structural Engineering, 16(6), 1011-1034.
20. 20. SAP2000, 2016. Integrated Finite Element Analysis and Design of Structures, Computers and Structures Inc., Berkeley, CA, USA.
21. 21. Kim, P., 2017. Matlab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence, Apress, Berkeley.
22. 22. Cooper, D.C., 2011. Introduction to Neuroscience. Donald C. Cooper Ph. D..
23. 23. Burden, F., Winkler D., 2008. Bayesian Regularization of Neural Networks. in: Livingstone D.J. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 458. Humana Press.
24. 24. Dicleli, M., Erhan, S., 2009. Live Load Distribution Formulas for Single-Span Prestressed Concrete Integral Abutment Bridge Girders. Journal of Bridge Engineering, 14(6), 472-486.