Straightforward Prediction for Responses of the Concrete Shear Wall Buildings Subject to Ground Motions Using Machine Learning Algorithms

Document Type : Original Article

Authors

1 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

2 Structural Engineering, School of civil engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran

Abstract

The prediction of responses of the reinforced concrete shear walls subject to strong ground motions is critical in designing, assessing, and deciding the recovery strategies. This study evaluates the ability of regression models and a hybrid technique (ANN-SA model), the artificial neural network (ANN), and Simulated Annealing (SA), to predict responses of the reinforced concrete shear walls subject to strong ground motions. To this end, four buildings (15, 20, 25, and 30-story) with concrete shear walls were analyzed in OpenSees.150 seismic records are used to generate a comprehensive database of input (characteristics of records) and output (responses). The maximum acceleration, maximum velocity, and earthquake characteristics are used as predictors. Different machine learning models are used, and the accuracy of the models in identifying the responses of the shear walls is compared. The sensitivity of input variables to the seismic demand model is investigated. It has been seen from the results that the ANN-SA model has reasonable accuracy in the prediction.

Keywords

References

  1. Baltacıoğlu, A. K., Öztürk, B. A. K. İ., Civalek, Ö. M. E. R., Akgöz, B. E. K. İ. R. "Is Artificial Neural Network Suitable for Damage Level Determination of RC-Structures?" International Journal of Engineering and Applied Sciences, Vol. 2, No. 3, (2010), 71-81.
  2. Thaler, D., Stoffel, M., Markert, B., Bamer, F. "Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering." Earthquake Engineering & Structural Dynamics, (2021). https://doi.org/10.1002/eqe.3432
  3. Goswami, S., Anitescu, C., Chakraborty, S., Rabczuk, T.: Transfer learning enhanced physics informed neural network for phase-field modeling of fracture, Theoretical and Applied Fracture Mechanics, 2020, 106, Article number 102447
  4. H. Guo, X. Zhuang, and T. Rabczuk: A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials and Continua, Vol. 59, No. 2, (2019), 433-456.
  5. C. Anitescu, E. Atroshchenko, N. Alajlan, and T. Rabczuk: Artificial Neural Network methods for the solution of second order boundary value problems. Computers, Materials and Continua, Vol. 59, No. 1, (2019), 345-359.
  6. Nguyen-Thanh, V.M., Zhuang, X., Rabczuk, T.: A deep energy method for finite deformation hyperelasticity, European Journal of Mechanics, A/Solids, 2020, 80, Article number 103874
  7. Mangalathu, S., Jeon, J. S. "Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques.” Engineering Structures, Vol. 160, (2018), 85-94. https://doi.org/10.1016/j.engstruct.2018.01.008
  8. Ebtehaj, I., Bonakdari, H., Es-haghi, M. S. "Design of a hybrid ANFIS–PSO model to estimate sediment transport in open channels.” Iranian Journal of Science and Technology, Transactions of Civil Engineering, Vol. 43, No. 4, (2019), 851-857. https://doi.org/10.1007/s40996-018-0218-9
  9. Safari, M. J. S., Ebtehaj, I., Bonakdari, H., Es-haghi, M. S. "Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling." Journal of Hydrology, Vol. 577, (2019), 123951. https://doi.org/10.1016/j.jhydrol.2019.123951
  10. Stoffel, M., Bamer, F., Markert, B. "Artificial Neural Networks and intelligent finite elements in non-linear structural mechanics." Thin-Walled Structures, Vol. 131, (2018), 102-106. https://doi.org/10.1016/j.tws.2018.06.035   
  11. Sun, H., Burton, H. V., Huang, H. "Machine learning applications for building structural design and performance assessment: state-of-the-art review.” Journal of Building Engineering, (2020), 101816. https://doi.org/10.1016/j.jobe.2020.101816
  12. Aydin, E., Öztürk, B., Guney, D. "Sensitivity analyses of variations on seismic response via viscous damper placement in planar building structures", 10th International Conference on Urban Earthquake Engineering, Tokyo, Japan, (Mar. 1-2, 2013), 2013.‏
  13. Khaleghi, M., Salimi, J., Farhangi, V., Moradi, M. J., Karakouzian, M. "Application of Artificial Neural Network to Predict Load Bearing Capacity and Stiffness of Perforated Masonry Walls." Civil Engineering, Vol. 2, No. 1, (2021), 48-67. https://doi.org/10.3390/civileng2010004
  14. Mangalathu, S., Jeon, J. S. "Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study." Journal of Structural Engineering, Vol. 145, No. 10, (2019), 04019104. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002402
  15. Siam, A., Ezzeldin, M., El-Dakhakhni, W. "Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls."  Structures, Vol. 22, (2019), 252-265. https://doi.org/10.1016/j.istruc.2019.06.017
  16. Kiani, J., Camp, C., Pezeshk, S. "On the application of machine learning techniques to derive seismic fragility curves." Computers & Structures, Vol. 218, (2019), 108-122. https://doi.org/10.1016/j.compstruc.2019.03.004
  17. Gaba, A., Jana, A., Subramaniam, R., Agrawal, Y., Meleet, M. Analysis and Prediction of Earthquake Impact-a Machine Learning approach. 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India, (2019). https://doi.org/10.1109/CSITSS47250.2019.9031026
  18. Burton, H. V., Sreekumar, S., Sharma, M., Sun, H. "Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators." Structural Safety, Vol. 68, (2017), 85-96. https://doi.org/10.1016/j.strusafe.2017.05.009
  19. Zhang, Y., Burton, H. V., Sun, H., Shokrabadi, M. "A machine learning framework for assessing post-earthquake structural safety" Structural Safety, Vol. 72, (2018), 1-16. https://doi.org/10.1016/j.strusafe.2017.12.001
  20. Zhang, Y., Burton, H. V. "Pattern recognition approach to assess the residual structural capacity of damaged tall buildings." Structural Safety, Vol. 78, (2019), 12-22. https://doi.org/10.1016/j.strusafe.2018.12.004
  21. Mazzoni S., McKenna F., Scott M. H., Fenves G. L. OpenSees command language manual. Pacific Earthquake Engineering Research (PEER) Center. 2006.
  22. McKenna, F., Scott, M. H., Fenves, G. L. "Nonlinear finite-element analysis software architecture using object composition." Journal of Computing in Civil Engineering, Vol. 24, No. 1, (2010), 95-107. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000002
  23. ACI A. 318-19. Building Code Requirements for Structural Concrete. ACI: Farmington Hills, MI, USA. 2019.
  24. American Society for Civil Engineers (ASCE), "Minimum Design Loads and Associated Criteria for Buildings and Other Structures", ASCE/SEI 7-16, 2016.  https://doi.org/10.1061/9780784414248
  25. Barkhordari, M. S., Tehranizadeh, M., Scott, M. H. "Numerical modelling strategy for predicting the response of reinforced concrete walls using Timoshenko theory." Magazine of Concrete Research, (2021), 1-23. https://doi.org/10.1680/jmacr.19.00542
  26. Kolozvari, K., Orakcal, K., Wallace, J. W. "New opensees models for simulating nonlinear flexural and coupled shear-flexural behavior of RC walls and columns." Computers & Structures, Vol. 196, (2018), 246-262. https://doi.org/10.1016/j.compstruc.2017.10.010
  27. Mander J. B., Priestley M. J., Park R. "Theoretical stress-strain model for confined concrete." Journal of structural engineering. Vol. 114, No. 8, (1988), 1804-26. https://doi.org/10.1061/(asce)0733-9445(1988)114:8(1804)
  28. Ancheta T. D., Darragh R. B., Stewart J. P., Seyhan E., Silva W. J., Chiou B. S., Wooddell K. E., Graves R. W., Kottke A. R., Boore D. M., Kishida T. PEER 2013/03: PEER NGA-West2 Database. Pacific Earthquake Engineering Research. 2013.
  29. Tran, T. A. "Experimental and analytical studies of moderate aspect ratio reinforced concrete structural walls." Doctoral dissertation, UCLA, (2012).‏
  30. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, (2009).
  31. Hoerl A. E., Kennard R. W. "Ridge regression: Biased estimation for nonorthogonal problems." Technometrics. Vol. 12, No. 1, (1970), 55-67. https://doi.org/10.1080/00401706.1970.10488634
  32. Tibshirani R. "Regression shrinkage and selection via the lasso." Journal of the Royal Statistical Society: Series B (Methodological). Vol. 58, No. 1, (1996), 267-88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
  33. Zou H., Hastie T. "Regularization and variable selection via the elastic net." Journal of the Royal Statistical Society: Series B (Statistical Methodology). Vol. 67, No. 2, (2005), 301-20. https://doi.org/10.1111/j.1467-9868.2005.00503.x
  34. Giussani A. Applied Machine Learning with Python. EGEA spa; 2020.
  35. Huber P.J. Robust Estimation of a Location Parameter. In: Kotz S., Johnson N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics). Springer, New York, NY, (1992). https://doi.org/10.1007/978-1-4612-4380-9_35
  36. Fischler M. A., Bolles R.C. "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography." Communications of the ACM, Vol. 24, No. 6, (1981), 381-395. https://doi.org/10.1145/358669.358692
  37. Delgoshaei A., Rabczuk T., Ali A., Ariffin M. K. "An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources." Annals of Operations Research, Vol. 259, (2017), 85-117. https://doi.org/10.1007/s10479-016-2336-8
  38. Scales L. E. Introduction to non-linear optimization. Macmillan International Higher Education; (1985). https://doi.org/10.1007/978-1-349-17741-7
  39. Kirkpatrick S., Gelatt C. D., Vecchi M. P. "Optimization by simulated annealing." Science, Vol. 220, (1983), 671-680. https://doi.org/10.1126/science.220.4598.671
  40. Zain A. M, Haron H., Sharif S. "Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining." Engineering with Computers, Vol. 27, No. 3, (2011), 251-259. https://doi.org/10.1007/s00366-010-0195-5
  41. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J. "Scikit-learn: Machine learning in Python." Journal of Machine Learning Research, Vol. 12, (2011), 2825-2830.
  42. Vu-Bac N., Lahmer T., Zhuang X., Nguyen-Thoi T., Rabczuk T. "A software framework for probabilistic sensitivity analysis for computationally expensive models." Advances in Engineering Software. Vol. 100, (2016), 19-31. https://doi.org/10.1016/j.advengsoft.2016.06.005
  43. Chakroborty S., Roy R. "Role of ground motion characteristics on inelastic seismic response of irregular structures." Journal of Architectural Engineering. Vol. 22, No. 1, (2016), B4015007. https://doi.org/10.1061/(asce)ae.1943-5568.0000185
  44. Fatemi H., Paultre P., Lamarche C. P. "Experimental Evaluation of Inelastic Higher-Mode Effects on the Seismic Behavior of RC Structural Walls." Journal of Structural Engineering, Vol. 146, No. 4, (2020), 04020016. https://doi.org/10.1061/(asce)st.1943-541x.0002509
  45. Barkhordari, M. S., Tehranizadeh, M. "Ranking Passive Seismic Control Systems by Their Effectiveness in Reducing Responses of High-Rise Buildings with Concrete Shear Walls Using Multiple-Criteria Decision Making." International Journal of Engineering, Transactions B: Applications, Vol. 33, No. 8, (2020), 1479-1490.https://doi.org/10.5829/ije.2020.33.08b.06

 

International Journal of Engineering
Volume 34, Issue 7
TRANSACTIONS A: Basics
July 2021
Pages 1586-1601

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