{"id":2238,"date":"2026-04-03T16:11:00","date_gmt":"2026-04-03T08:11:00","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2238"},"modified":"2026-05-24T14:16:03","modified_gmt":"2026-05-24T06:16:03","slug":"response-prediction-analysis-of-rc-frame-structures-using-support-vector-machine-algorithm","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=response-prediction-analysis-of-rc-frame-structures-using-support-vector-machine-algorithm","title":{"rendered":"Response Prediction Analysis of RC Frame Structures Using Support Vector Machine Algorithm"},"content":{"rendered":"\n
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<\/i> 2025<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 28, Issue 2<\/a><\/p>\n<\/div>\n\n\n\n

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Shouliang Qu1<\/sup><\/i><\/a>, Xuefeng Jiao2<\/sup>, Xi Chen1<\/sup>, and Jun Zhao1<\/sup><\/p>\n\n\n\n

1<\/sup>China Second Metallurgy Group Corporation Limited, Baotou 014000, China<\/p>\n\n\n\n

2<\/sup>Shandong Huake Planning Architectural Design Co. Ltd, Liaocheng, 252000, China<\/p>\n<\/div>\n\n\n\n

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Received: December 11, 2023
Accepted: February 22, 2024
Publication Date: April 3, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Time consumption to calculate hyper-parameters of SVM prediction model based on.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202502_28(2).0013<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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To more accurately predict the structural response under seismic actions, this paper takes two reinforced concrete frame structures with 9- and 11-stories as examples and utilizes the Support vector machine (SVM) algorithm to analyze the efficiency of 6 scalar and 15 vector intensity measures (IMs) in predicting the response of RC frame structures. The time required to determine hyperparameters using a grid search optimization algorithm is also provided. The results show that when using scalar IMs for prediction, SI<\/em> can better predict the structural response of 9-story structure, Sa<\/sub><\/em>(T) can better predict the structural response of 11-story structure, and the hyperparameters of the model can be determined in a relatively short time. When using vector IMs for prediction, the best vector IMs for prediction is [PGV, Sa<\/sub><\/em>(T)], and the determination coefficient R2<\/sup> of the training and testing sets reaches 0.96 or above, with the standard deviation (\u03b2) below 0.3 . Compared with the best prediction results based on scalar IMs, the \u03b2 is significantly reduced, both by more than 20%. This conclusion provides a theoretical basis for quantitatively evaluating the degree of structural damage caused by seismic motion.<\/p>\n\n\n\n

Keywords: Support vector machine; grid search optimization; intensity measures; structural response; prediction model<\/em><\/p>\n\n\n\n

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  1. [1] S.-K. Au and J. L. Beck, (2003) \u201cSubset simulation and its application to seismic risk based on dynamic analysis” Journal of engineering mechanics 129(8): 901\u2013917. DOI: 10.1061\/(ASCE)0733-9399(2003)129:8(901).<\/li>\n
  2. [2] F. Jalayer and J. Beck, (2008) \u201cEffects of two alternative representations of ground-motion uncertainty on probabilistic seismic demand assessment of structures” Earthquake engineering & structural dynamics 37(1): 61\u201379. DOI: 10.1002\/eqe.745.<\/li>\n
  3. [3] S. K. Shahi and J. W. Baker, (2011) \u201cAn empirically calibrated framework for including the effects of near-fault directivity in probabilistic seismic hazard analysis” Bulletin of the Seismological Society of America 101(2): 742\u2013755. DOI: 10.1785\/0120100090.<\/li>\n
  4. [4] B. Huang, S. G\u00fcnay, and W. Lu, (2022) \u201cSeismic assessment of freestanding ceramic vase with shaking table testing and performance-based earthquake engineering” Journal of Earthquake Engineering 26(15): 7956\u20137978. DOI: 10.1080\/13632469.2021.1979132.<\/li>\n
  5. [5] R. Montuori, E. Nastri, V. Piluso, and P. Todisco, (2020) \u201cA simplified performance based approach for the evaluation of seismic performances of steel frames” Engineering Structures 224: 111222. DOI: 10.1016\/j.engstruct.2020.111222.<\/li>\n
  6. [6] A. Filiatrault, D. Perrone, R. J. Merino, and G. M. Calvi, (2021) \u201cPerformance-based seismic design of nonstructural building elements” Journal of Earthquake Engineering 25(2): 237\u2013269. DOI: 10.1080\/13632469.2018.1512910.<\/li>\n
  7. [7] V. Chandrikka and D. S. Rajkumar. \u201cDevelopment of Sustainable Earthquake Resistant Building for Future Generations\u201d. In: International Conference on Civil Engineering Innovative Development in Engineering Advances. Springer, 239\u2013249. DOI: 10.1007\/978-981-99-6233-4_22.<\/li>\n
  8. [8] N. Afzali and S. Hamzehloo, (2018) \u201cEvaluating the role of recycling materials in construction industry (Case study: City of Tehran)” Journal of Environmental Friendly Materials 2(2): 49\u201358.<\/li>\n
  9. [9] Y. Lieping, M. Qianli, and M. Zhiwei, (2009) \u201cStudy on earthquake intensities for seismic analysis of structures” Journal of Earthquake Engineering and Engineering Vibration 29(4): 9\u201322.<\/li>\n
  10. [10] J. W. Baker and C. A. Cornell, (2008) \u201cVector-valued intensity measures for pulse-like near-fault ground motions” Engineering structures 30(4): 1048\u20131057. DOI: 10.1016\/j.engstruct.2007.07.009.<\/li>\n
  11. [11] Y. Zhou, N. Su, and X. Lu, (2013) \u201cStudy on intensity measure of incremental dynamic analysis for high-rise structures” Jianzhu Jiegou Xuebao(Journal of Building Structures) 34(2): 53\u201360.<\/li>\n
  12. [12] Z. Zi-lan, S. Yi-yao, Z. Li-bin, Z. Cheng-ming, Z. Mi, and D. Xiu-li, (2020) \u201cGround motion intensity measures and dynamic response indexes of metro station structures” Chinese Journal of Geotechnical Engineering 42(3): 486\u2013494. DOI: 10.11779\/CJGE202003010.<\/li>\n
  13. [13] Y. YH and W. XP, (2020) \u201cValidity assessment of a new vector-valued intensity parameter of earthquake ground motion” Journal of Jiangxi University of Science and Technology 41(3): 1\u20138.<\/li>\n
  14. [14] A. Yazdani and K. Yazdannejad, (2019) \u201cEstimation of the seismic demand model for different damage levels” Engineering Structures 194: 183\u2013195. DOI: 10.1016\/j.engstruct.2019.05.071.<\/li>\n
  15. [15] F. Jalayer, J. Beck, and F. Zareian, (2012) \u201cAnalyzing the sufficiency of alternative scalar and vector intensity measures of ground shaking based on information theory” Journal of Engineering Mechanics 138(3): 307\u2013316. DOI: 10.1061\/(ASCE)EM.1943-7889.0000327.<\/li>\n
  16. [16] M. o. H. China and U. R. D. of the People\u2019s Republic of. Code for seismic design of buildings.GB50011-2010. Standard. 2010.<\/li>\n
  17. [17] S. J. Venture, (2000) \u201cState of the art report on systems performance of steel moment frames subject to earthquake ground shaking” FEMA 355C:<\/li>\n
  18. [18] B. Standard, (2005) \u201cEurocode 8: Design of structures for earthquake resistance” Part 1: 1998\u20131.<\/li>\n
  19. [19] Y. Can-tian, X. Li-li, L. Ai-qun, Z. De-min, and L. Lide, (2018) \u201cIntensity measures for seismically isolated tall buildings” Engineering Mechanics 35(8): 21\u201329. DOI: 10.6052\/j.issn.1000-4750.2017.07.0531.<\/li>\n
  20. [20] P. Tothong and C. A. Cornell, (2008) \u201cStructural performance assessment under near-source pulse-like ground motions using advanced ground motion intensity measures” Earthquake Engineering Structural Dynamics 37(7): 1013\u20131037. DOI: 10.1002\/eqe.792.<\/li>\n
  21. [21] L. Ye, Q. Ma, Z. Miao, H. Guan, and Y. Zhuge, (2013) \u201cNumerical and comparative study of earthquake intensity indices in seismic analysis” The Structural Design of Tall and Special Buildings 22(4): 362\u2013381. DOI: 10.1002\/tal.693.<\/li>\n
  22. [22] D. Yang, J. Pan, and G. Li, (2009) \u201cNon-structurespecific intensity measure parameters and characteristic period of near-fault ground motions” Earthquake Engineering Structural Dynamics 38(11): 1257\u20131280. DOI: 10.1002\/eqe.889.<\/li>\n
  23. [23] H. Ebrahimian, F. Jalayer, A. Lucchini, F. Mollaioli, and G. Manfredi, (2015) \u201cPreliminary ranking of alternative scalar and vector intensity measures of ground shaking” Bulletin of Earthquake Engineering 13: 2805\u20132840. DOI: 10.1007\/s10518-015-9755-9.<\/li>\n
  24. [24] Y. Xu, X. Lu, Y. Tian, and Y. Huang, (2022) \u201cRealtime seismic damage prediction and comparison of various ground motion intensity measures based on machine learning” Journal of Earthquake Engineering 26(8): 4259\u20134279. DOI: 10.1080\/13632469.2020.1826371.<\/li>\n
  25. [25] Y. Zhou, Y. Zhang, R. Pang, and B. Xu, (2021) \u201cSeismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine” Soil Dynamics and Earthquake Engineering 144: 106587. DOI: 10.1016\/j.soildyn.2021.106587.<\/li>\n
  26. [26] D. BR and D. K, (2018) \u201cStudy on the relationship between seismic intensity and ground motion parameters considering uncertainty” Earthquake Engineering and Engineering Dynamics 38(2): 106\u2013114. DOI: 10.13197\/j.eeev.2018.02.106.dingbr.012.<\/li>\n
  27. [27] C. ZW. \u201cResearch on the most unfavorable design ground motions of building structures”. (Thesis). 2010.<\/li>\n
  28. [28] L. QH and H. JJ, (2022) \u201cMethod for ranking pulselike ground motions according to damage potential for reinforced concrete frame structures” Buildings 12: 1\u201318. DOI: 10.3390\/buildings12060754.<\/li>\n
  29. [29] A. M. Reinhorn, H. Roh, M. V. Sivaselvan, S. K. Kunnath, R. Valles, A. Madan, C. Li, R. Lobo, and Y. Park. IDARC2D version 7.0: a program for the inelastic damage analysis of structures. MCEER, 2009.<\/li>\n
  30. [30] V. Vapnik. The nature of statistical learning theory. Springer science business media, 2013.<\/li>\n
  31. [31] V. Vapnik. Statistical learning theory. New York: Wiley, 1998.<\/li>\n
  32. [32] W. Zhang, Y. Fan, H. Han, B. Zhang, and X. Cui, (2019) \u201cProduct Sale Forecast Based on Support Vector Machine Optimized by Cross Validation and Grid Search” Computer Systems Applications 28(5): 1\u20139.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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