DOI QR코드

DOI QR Code

Ensemble techniques and hybrid intelligence algorithms for shear strength prediction of squat reinforced concrete walls

  • Mohammad Sadegh, Barkhordari (Department of Civil and Environmental Engineering, Amirkabir University of Technology) ;
  • Leonardo M., Massone (Department of Civil Engineering, University of Chile)
  • 투고 : 2022.09.16
  • 심사 : 2022.11.17
  • 발행 : 2023.01.25

초록

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R2= 0.99) that are superior to other models.

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과제정보

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

참고문헌

  1. ACI-318 (2014), "Building Code Requirements for Structural Concrete (ACI 318-14): An ACI Standard; Commentary on Building Code Requirements for Structural Concrete (ACI 318R-14)", American Concrete Institute.
  2. Aggarwal, Y., Aggarwal, P., Sihag, P., Pal, M. and Kumar, A. (2019), "Estimation of punching shear capacity of concrete slabs using data mining techniques", Int. J. Eng., 32(7), 908-914. https://doi.org/10.5829/ije.2019.32.07a.02
  3. Amezquita-Sancheza, J., M. Valtierra-Rodriguez, and H. Adeli (2020), "Machine learning in structural engineering", Scientia Iranica, 27(6), 2645-2656. https://doi.org/10.24200/sci.2020.22091
  4. Barkhordari, M. and M. Es-haghi (2021), "Straightforward prediction for responses of the concrete shear wall buildings subject to ground motions using machine learning algorithms", Int. J. Eng., 34(7), 1586-1601. https://doi.org/10.5829/ije.2021.34.07a.04
  5. Barkhordari, M.S. and M. Tehranizadeh (2021), "Response estimation of reinforced concrete shear walls using artificial neural network and simulated annealing algorithm", Structures, 34, 1155-1168. https://doi.org/10.1016/j.istruc.2021.08.053.
  6. Barkhordari, M.S., D.C. Feng, and M. Tehranizadeh (2022), "Efficiency of hybrid algorithms for estimating the shear strength of deep reinforced concrete beams", Periodica Polytechnica Civil Eng., 66(2), 398-410. https://doi.org/10.3311/PPci.19323
  7. Barkhordari, M.S., M. Tehranizadeh, and M.H. Scott (2021a), "Numerical modelling strategy for predicting the response of reinforced concrete walls using Timoshenko theory", Magazine Concr. Res., 73(19), 1-23. https://doi.org/10.1680/jmacr.19.00542
  8. Barkhordari, M.S., M. Tehranizadeh, and M.H. Scott (2021b), "Numerical modelling strategy for predicting the response of reinforced concrete walls using Timoshenko theory", Magazine Concr. Res., 1-23. https://doi.org/10.1680/jmacr.19.00542.
  9. Brownlee, J. (2018), Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions. Machine Learning Mastery.
  10. Chen, X.L., Fu, J.P., Yao, J.L. and Gan, J.F. (2018), "Prediction of shear strength for squat RC walls using a hybrid ANN-PSO model", Eng. Comput., 34(2), 367-383. https://doi.org/10.1007/s00366-017-0547-5
  11. Committee, A. (2019), "Building code requirements for structural concrete (ACI 318-19) and commentary": American Concrete Institute.
  12. Committee, N.S. (2005), "Seismic design criteria for structures, systems, and components in nuclear facilities", Am. Soc. Civil Eng., Reston, VA. https://doi.org/10.1061/9780784407622.
  13. Dietterich, T.G. (2000), "Ensemble methods in machine learning", Int. Workshop Multiple Classifier Syst., 1-15. https://doi.org/10.1007/3-540-45014-9_1.
  14. Duman, S., Kahraman, H.T., Guvenc, U. and Aras, S. (2021), "Development of a Levy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems", Soft Comput., 25(8), 6577-6617. https://doi.org/10.1007/s00500-021-05654-z
  15. Feng, D.C., Wang, W.J., Mangalathu, S. and Taciroglu, E. (2021), "Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls", J. Struct. Eng., 147(11), 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115
  16. Gondia, A., M. Ezzeldin, and W. El-Dakhakhni (2020), "Mechanics-guided genetic programming expression for shear-strength prediction of squat reinforced concrete walls with boundary elements", J. Struct. Eng., 146(11), 04020223. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002734
  17. Guan, X., Burton, H., Shokrabadi, M. and Yi, Z. (2021), "Seismic drift demand estimation for steel moment frame buildings: From mechanics-based to data-driven models", J. Struct. Eng., 147(6), 04021058. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003004
  18. Gulec, C.K. (2009), Performance-Based Assessment and Design of Squat Reinforced Concrete Shear Walls, State University of New York at Buffalo.
  19. Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S. and Al-Atabany, W. (2021), "Honey badger algorithm: New metaheuristic algorithm for solving optimization problems", Math. Comput. Simul., 192, 84-110. https://doi.org/10.1016/j.matcom.2021.08.013
  20. Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E. and Weinberger, K.Q. (2017), "Snapshot ensembles: Train 1, get m for free", arXiv preprint arXiv:1704.00109.
  21. Jaeger, S. (2020), "The golden ratio of learning and momentum", arXiv preprint arXiv:2006.04751.
  22. Kassem, W. (2015), "Shear strength of squat walls: A strut-and-tie model and closed-form design formula", Eng. Struct., 84, 430-438. https://doi.org/10.1016/j.engstruct.2014.11.027
  23. Kolozvari, K., Kalbasi, K., Orakcal, K., Massone, L. M. and Wallace, J. (2019), "Shear-flexure-interaction models for planar and flanged reinforced concrete walls", Bull. Earthq. Eng., 17(12), 6391-6417. https://doi.org/10.1007/s10518-019-00658-5
  24. Loshchilov, I. and F. Hutter (2016), "Sgdr: Stochastic gradient descent with warm restarts", arXiv preprint arXiv:1608.03983.
  25. Lu, W. and R. Paffenroth (2021), "Neural network ensembles: Theory, training, and the importance of explicit diversity", J. Machine Learning Res. https://doi.org/10.48550/arXiv.2109.14117.
  26. Lundberg, S.M. and S.I. Lee (2017), "A unified approach to interpreting model predictions", Adv. Neural Inform. Proc. Syst., 30.
  27. Ma, J. and B. Li (2018), "Experimental and analytical studies on h-shaped reinforced concrete squat walls", ACI Struct. J., 115(2). https://doi.org/10.14359/51701144
  28. Ma, J., C.L. Ning, and B. Li (2020), "Peak shear strength of flanged reinforced concrete squat walls", J. Struct. Eng., 146(4), 04020037. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002575
  29. Massone, L.M. and F. Melo (2018), "General solution for shear strength estimate of RC elements based on panel response", Eng. Struct., 172, 239-252. https://doi.org/10.1016/j.engstruct.2018.06.038
  30. Massone, L.M. and M.A. Ulloa (2014), "Shear response estimate for squat reinforced concrete walls via a single panel model", Earthq. Struct., 7(5), 647-665. https://doi.org/10.12989/eas.2014.7.5.647
  31. Massone, L.M., C.N. Lopez, and K. Kolozvari (2021), "Formulation of an efficient shear-flexure interaction model for planar reinforced concrete walls", Eng. Struct., 243, 112680. https://doi.org/10.1016/j.engstruct.2021.112680
  32. Mohammed, S.J., H.A. Abdel-khalek, and S.M. Hafez (2021), "Predicting performance measurement of residential buildings using an artificial neural network", Civil Eng. J., 7(3), 461-476. https://doi.org/10.28991/cej-2021-03091666
  33. Moradi, M.J., Roshani, M.M., Shabani, A. and Kioumarsi, M. (2020), "Prediction of the load-bearing behavior of SPSW with rectangular opening by RBF network", Appl. Sci., 10(3), 1185. https://doi.org/10.3390/app10031185
  34. Naimi, A.I. and L.B. Balzer (2018), "Stacked generalization: an introduction to super learning", Eur. J. Epidemiol., 33(5), 459-464. https://doi.org/10.1007/s10654-018-0390-z
  35. Nguyen, D.D., Tran, V.L., Ha, D.H., Nguyen, V.Q. and Lee, T.H. (2021), "A machine learning-based formulation for predicting shear capacity of squat flanged RC walls", Structures, 29, 1734-1747. https://doi.org/10.1016/j.istruc.2020.12.054.
  36. Ning, C.L. and B. Li (2017), "Probabilistic development of shear strength model for reinforced concrete squat walls", Earthq. Eng. Struct. Dyn., 46(6), 877-897. https://doi.org/10.1002/eqe.2834
  37. Pizarro, P.N. and L.M. Massone (2021), "Structural design of reinforced concrete buildings based on deep neural networks", Eng. Struct., 241, 112377. https://doi.org/10.1016/j.engstruct.2021.112377
  38. Pizarro, P.N., et al. (2021), "Use of convolutional networks in the conceptual structural design of shear wall buildings layout", Eng. Struct., 239, 112311. https://doi.org/10.1016/j.engstruct.2021.112311
  39. Rojas, F., J. Anderson, and L. Massone (2016), "A nonlinear quadrilateral layered membrane element with drilling degrees of freedom for the modeling of reinforced concrete walls", Eng. Struct., 124, 521-538. https://doi.org/10.1016/j.engstruct.2016.06.024
  40. Siam, A.S., M. Ezzeldin, and W. El-Dakhakhni (2019), "Reliability of displacement capacity prediction models for reinforced concrete block shear walls", Structures, 20, 385-398. https://doi.org/10.1016/j.istruc.2019.05.002.
  41. Sutton, A.K. and M.J. Krashes (2020), "Integrating hunger with rival motivations", Trends Endocrinol. Metabolism, 31(7), 495-507. https://doi.org/10.1016/j.tem.2020.04.006
  42. Yang, Y., Chen, H., Heidari, A.A. and Gandomi, A.H. (2021), "Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts", Expert Syst. Appl., 177, 114864. https://doi.org/10.1016/j.eswa.2021.114864