A swift neural network-based algorithm for demand estimation in concrete moment-resisting buildings

Salkhordeh, Mojtaba; Mirtaheri, Masoud

doi:10.52547/nmce.6.3.28

Volume 6, Issue 3 (3-2022) NMCE 2022, 6(3): 28-36 | Back to browse issues page

‎ 10.52547/nmce.6.3.28

‎ 20.1001.1.23454296.2022.6.3.7.2

A swift neural network-based algorithm for demand estimation in concrete moment-resisting buildings

Mojtaba Salkhordeh¹, Masoud Mirtaheri ^*

1- Mojtaba Salkhordeh, Ph.D. student, Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2- Masoud Mirtaheri, Associate professor, Department of civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran. , mmirtaheri@kntu.ac.ir

Abstract: (692 Views)

Rapid evaluation of demand parameters of different types of buildings is crucial for social restoration after damaging earthquakes. Previous studies proposed numerous methodologies to measure the performance of buildings for assessing the potential risk under the seismic hazard. However, time-consuming Nonlinear Response History Analysis (NRHA) barricaded implementing a prompt loss estimation for emergency confronting actions. The present study proposes a swift framework for demand estimation in concrete moment-resisting buildings using artificial neural networks. For this purpose, a simplified model is developed based on the HAZUS method. To eliminate the required time-consuming NRHA from the post-earthquake actions, Artificial Neural Networks (ANNs) are used. Before the event, ANNs are studied to estimate the demand parameters using a set of time-history analyses. This study applies to a suite of 111 earthquake events, originally developed in the SAC project and uniformly scaled from 0.1 g to 1.5 g , to achieve a generalized prediction model. Bayesian Optimization (BO) algorithm is carried out to tune the architecture of the NNs. Results reveal that the presented approach is reliable for predicting the structural response, and is cost-effective compared to the conventional NRHA. This framework can be implemented in the body of a risk assessment platform to expedite the postearthquake actions required for crisis management.

Keywords: Demand estimation, Artificial Neural Networks, Concrete moment-resisting buildings, Risk assessment, HAZUS

Full-Text [PDF 2781 kb] (332 Downloads)

Type of Study: Research | Subject: General
Received: 2021/09/3 | Revised: 2021/10/28 | Accepted: 2021/10/29 | ePublished ahead of print: 2021/11/3

References

1. R. Karami-Mohammadi, M. Mirtaheri, M. Salkhordeh, M.A. Hariri-Ardebili, Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors, Sensors. 20 (2020) 1731. [DOI:10.3390/s20061731]

2. K. Goda, T. Kiyota, R.M. Pokhrel, G. Chiaro, T. Katagiri, K. Sharma, S. Wilkinson, The 2015 Gorkha Nepal earthquake: insights from earthquake damage survey, Front. Built Environ. 1 (2015) 8. https://doi.org/10.3389/fbuil.2015.00008 [DOI:https://doi.org/10.3389/fbuil.2015.00008.]

3. T. Parsons, C. Ji, E. Kirby, Stress changes from the 2008 Wenchuan earthquake and increased hazard in the Sichuan basin, Nature. 454 (2008) 509-510. https://doi.org/10.1038/nature07177 [DOI:https://doi.org/10.1038/nature07177.]

4. N. Mori, T. Takahashi, T. Yasuda, H. Yanagisawa, Survey of 2011 Tohoku earthquake tsunami inundation and run-up, Geophys. Res. Lett. 38 (2011). https://doi.org/10.1029/2011GL049210 [DOI:https://doi.org/10.1029/2011GL049210.]

5. M. Mirtaheri, M. Salkhordeh, M. Mohammadgholiha, A System Identification-Based Damage-Detection Method for Gravity Dams, Shock Vib. 2021 (2021). [DOI:10.1155/2021/6653254]

6. L. Ye, X. Lu, Y. Li, Design objectives and collapse prevention for building structures in mega-earthquake, Earthq. Eng. Eng. Vib. 9 (2010) 189-199. https://doi.org/10.1007/s11803-010-0005-5 [DOI:https://doi.org/10.1007%2Fs11803-010-0005-5.]

7. A. Hasegawa, K. Yoshida, Y. Asano, T. Okada, T. Iinuma, Y. Ito, Change in stress field after the 2011 great Tohoku-Oki earthquake, Earth Planet. Sci. Lett. 355 (2012) 231-243. https://doi.org/10.1016/j.epsl.2012.08.042 [DOI:https://doi.org/10.1016/j.epsl.2012.08.042.]

8. X. Lu, B. Han, M. Hori, C. Xiong, Z. Xu, A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing, Adv. Eng. Softw. 70 (2014) 90-103. [DOI:10.1016/j.advengsoft.2014.01.010]

9. C. Rojahn, R.L. Sharpe, Earthquake damage evaluation data for California, Applied technology council, 1985.

10. N.I. of Building Sciences (Washington, U.S.F.E.M. Agency, Earthquake Loss Estimation Methodology:" HAZUS" Technical Manual, Federal Emergency Management Agency, 1997.

11. F.E.M.A. (FEMA), Multi-hazard loss estimation methodology earthquake model, HAZUS-MH MR3 Technical Manual, (2003).

12. W.D. Iwan, Drift spectrum: measure of demand for earthquake ground motions, J. Struct. Eng. 123 (1997) 397-404. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:4(397) [DOI:https://doi.org/10.1061/(ASCE)0733-9445(1997)123:4(397).]

13. D.M. Seyedi, P. Gehl, J. Douglas, L. Davenne, N. Mezher, S. Ghavamian, Development of seismic fragility surfaces for reinforced concrete buildings by means of nonlinear time-history analysis, Earthq. Eng. Struct. Dyn. 39 (2010) 91-108. https://doi.org/10.1002/eqe.939 [DOI:https://doi.org/10.1002/eqe.939.]

14. A.T.C. for the Federal Emergency Management Agency, Next-Generation Methodology for Seismic Performance Assessment of Buildings, Federal Emergency Management Agency, 2012.

15. J. Ruiz-Garcia, E. Miranda, Probabilistic estimation of residual drift demands for seismic assessment of multi-story framed buildings, Eng. Struct. 32 (2010) 11-20. https://doi.org/10.1016/j.engstruct.2009.08.010 [DOI:https://doi.org/10.1016/j.engstruct.2009.08.010.]

16. M. Salkhordeh, E. Govahi, M. Mirtaheri, Seismic fragility evaluation of various mitigation strategies proposed for bridge piers, Structures. 33 (2021) 1892-1905. [DOI:10.1016/j.istruc.2021.05.041]

17. R. Karami-Mohammadi, M. Mirtaheri, M. Salkhordeh, E. Mosaffa, G. Mahdavi, M.A. Hariri-Ardebili, Seismic mitigation of substation cable connected equipment using friction pendulum systems, Struct. Eng. Mech. 72 (2019) 785-796. [DOI:10.12989/sem.2019.72.6.785]

18. M. Yekrangnia, Application of Endurance Time method in evaluation of seismic performance of a typical sandwich panel building, J. Numer. Methods Civ. Eng. 5 (2020) 45-52. [DOI:10.52547/nmce.5.2.45]

19. M. Mirtaheri, M. Salkhordeh, S.M. S Kolbadi, H. Mirzaeefard, M.R. Razzaghian, Evaluation of 2D concentrically braced frames with cylindrical dampers subjected to near-field earthquake ground motions, Numer. Methods Civ. Eng. 4 (2020) 21-30. [DOI:10.52547/nmce.4.3.21]

20. R. Karami-Mohammadi, M. Mirtaheri, M. Salkhordeh, M. Hariri-Ardebili, A cost-effective neural network--based damage detection procedure for cylindrical equipment, Adv. Mech. Eng. 11 (2019) 1687814019866942. [DOI:10.1177/1687814019866942]

21. M. Salkhordeh, M. Mirtaheri, S. Soroushian, A decision-tree-based algorithm for identifying the extent of structural damage in braced-frame buildings, Struct. Control Heal. Monit. (2021) e2825. https://doi.org/10.1002/stc.2825 [DOI:10.1002/stc.2825 https://doi.org/https://doi.org/10.1002/stc.2825.]

22. E. Norouzi, S. Behzadi, Evaluating machine learning methods and satellite images to estimate combined climatic indices, Int. J. Numer. Methods Civ. Eng. 4 (2019) 30-38. [DOI:10.52547/nmce.4.1.30]

23. M.I. Jordan, T.M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science (80-. ). 349 (2015) 255-260. https://doi.org/10.1126/science.aaa8415 [DOI:https://doi.org/10.1126/science.aaa8415.]

24. B. Yegnanarayana, Artificial neural networks, PHI Learning Pvt. Ltd., 2009.

25. J.C. Weinstein, M. Sanayei, B.R. Brenner, Bridge damage identification using artificial neural networks, J. Bridg. Eng. 23 (2018) 4018084. [DOI:10.1061/(ASCE)BE.1943-5592.0001302]

26. J. Gu, M. Gul, X. Wu, Damage detection under varying temperature using artificial neural networks, Struct. Control Heal. Monit. 24 (2017) e1998. [DOI:10.1002/stc.1998]

27. B.K. Oh, Y. Park, H.S. Park, Seismic response prediction method for building structures using convolutional neural network, Struct. Control Heal. Monit. 27 (2020) e2519. https://doi.org/10.1002/stc.2519 [DOI:https://doi.org/10.1002/stc.2519.]

28. FEMA, Multi-Hazard Loss Estimation Methodology HAZUS-MH 2.1 Advanced Engineering Building Module (AEBM) Technical and User's Manual, Federal Emergency Management Agency Washington, DC, 2012.

29. M. Zhu, F. McKenna, M.H. Scott, OpenSeesPy: Python library for the OpenSees finite element framework, SoftwareX. 7 (2018) 6-11. [DOI:10.1016/j.softx.2017.10.009]

30. S. Mazzoni, F. McKenna, M.H. Scott, G.L. Fenves, others, OpenSees command language manual, Pacific Earthq. Eng. Res. Cent. 264 (2006).

31. A. Ranganathan, The levenberg-marquardt algorithm, Tutoral LM Algorithm. 11 (2004) 101-110.

32. V. Ahmadian, S.B. Beheshti Aval, E. Darvishan, Real-time damage detection of bridges using adaptive time-frequency analysis and ANN, Int. J. Numer. Methods Civ. Eng. 4 (2019) 49-61. [DOI:10.52547/nmce.4.1.49]

33. C.E. Rasmussen, Gaussian processes in machine learning, in: Summer Sch. Mach. Learn., 2003: pp. 63-71. [DOI:10.1007/978-3-540-28650-9_4]

34. J. Snoek, H. Larochelle, R.P. Adams, Practical bayesian optimization of machine learning algorithms, in: Adv. Neural Inf. Process. Syst., 2012: pp. 2951-2959.

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