基于机器学习的巨-子结构隔震体系地震损伤等级预测研究

摘要
图/表
参考文献(35)
相关文章 (15)

全文: PDF (2091 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要建立了巨-子结构隔震体系的三维有限元模型，基于地震动峰值加速度（PGA）对20条实际地震动记录进行调幅，在此基础上对考虑不同锈蚀状态下的巨-子结构隔震体系进行增量动力时程分析，得到了240组地震响应样本，探讨了钢材锈蚀对巨-子结构隔震体系抗震性能的影响。利用机器学习的方法将结构信息、地震动信息与结构的损伤等级相关联，给出了六种机器学习算法对巨-子结构隔震体系损伤等级的预测结果：极端梯度提升树、梯度提升树、随机森林、决策树的总体预测准确率均达到80%以上，其中极端梯度提升树算法表现最佳，准确率为86.6%且对不同损伤状态的预测精度也较高，支持向量机算法的总体预测准确率最低为60.3%。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李祥秀1
	宋笑彦2
	李小军1
	2
	李易2
	刘爱文1

关键词 ：巨-子结构隔震体系, 机器学习, 损伤等级, 锈蚀, 抗震性能, 增量动力时程分析

Abstract：The finite element model of the mega-sub isolation system was established in this paper. Incremental dynamic time history analyses of the mega-sub isolation system considering different corrosion states was carried out by inputting 20 actual ground motion records with amplitude modulation on the peak ground acceleration (PGA). 240 sets of seismic responses samples were obtained, and the effect of steel corrosion on the seismic performance of the mega-sub isolation system was discussed. Using machine learning method to correlate structural information, ground motion information, and structural damage level, the prediction results of six machine learning algorithms on the damage level of the mega-sub isolation system were given. The overall prediction accuracy of extreme gradient boosting tree, gradient boosting tree, random forest, and decision tree reached more than 80%. The extreme gradient boosting tree algorithm performed the best, with an accuracy rate of 86.6% and a higher prediction accuracy for different damage states. The prediction accuracy of the support vector machine algorithm was the lowest, with an accuracy rate of 60.3%.

Key words： mega-sub isolation system machine learning damage level corrosion seismic performance IDA

收稿日期: 2022-08-16 出版日期: 2023-10-28

引用本文:

李祥秀1，宋笑彦2，李小军1,2，李易2，刘爱文1. 基于机器学习的巨-子结构隔震体系地震损伤等级预测研究[J]. 振动与冲击, 2023, 42(20): 40-47.
LI Xiangxiu1，SONG Xiaoyan2，LI Xiaojun1,2，LI Yi2，LIU Aiwen1. Research on the earthquake damage level prediction of mega-sub isolation system based on machine learning. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(20): 40-47.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I20/40

[1] Feng M Q, Mita A. Vibration control of tall buildings using mega-sub configuration [J]. Journal of Engineering Mechanics, 1995, 121(10): 1082-1087.
[2] Chai W, Feng M Q. Vibration control of super tall buildings subjected to wind loads [J]. International Journal of Nonlinear Mechanics, 1997, 32(4): 665-668.
[3] 谭平,李祥秀,刘良坤, 等. 巨-子结构控制体系的减震机理及性能分析[J]. 土木工程学报, 2014, 47(11):55-63.
Tan Ping, Li Xiangxiu, Liu Liangkun, et al. Control mechanism and performance analysis of a mega-sub structure control system [J]. China Civil Engineering Journal, 2014, 47(11):55-63.
[4] 李祥秀, 王瑶, 李小军, 等. 速度脉冲地震动作用下巨-子结构隔震体系的振动台试验研究[J]. 应用基础与工程科学学报, 2021, 29(3): 633-644.
Li Xiangxiu, Wang Yao, Li Xiaojun, et al. Experimental studies on seismic performance of mega-sub isolation system subjected to near-fault ground motions with velocity pulses[J]. Journal of Basic Science and Engineering, 2021, 29(3): 633-644.
[5] Li X X, Tan P, Wang Y, et al. Shaking table test and numerical simulation on a mega-sub isolation system under near-fault ground motions with velocity pulses [J]. International Journal of Structural Stability and Dynamics, 2022, 22(2): 2250026.
[6] Li X X, Tan P, Liu A W, et al. Investigations on performances of a mega-sub isolation system under near-fault ground motions [J]. Advances in Structural Engineering, 2021, 24(15):3389-3404.
[7] Zhang X A, Wang D, Jiang J S. The controlling mechanism and the controlling effectiveness of passive mega-sub-controlled frame subjected to random wind loads [J]. Journal of Sound and Vibration, 2005, 283(3-5): 543-560.
[8] Zhang X A, Qin X J, Cherry S, Lian Y D, Zhang J L, Jiang J S. A new proposed passive mega-sub controlled structure and response control [J]. Journal of Earthquake Engineering, 2009, 13(2): 252-274.
[9] 连业达, 张洵安, 王朝霞. 巨、子结构质量比对新型有控结构建筑结构的影响研究[J]. 振动与冲击, 2007,26(8):113-115.
Lian Yeda, Zhang Xun’an, Wang Zhaoxia. Investigation in new building structure with different mass ratio [J]. Journal of Vibration and Shock, 2007, 26(8):113-115.
[10] 王肇民,邓洪洲,董军. 高层巨型框架悬挂结构体系抗震性能研究[J]. 建筑结构学报, 1999, 02(1): 23-30.
Wang Zhaomin, Deng Hongzhou, Dong Jun. A study of aseismic properties of huge frame suspended structure in tall buildings [J]. Journal of Building Structures, 1999, 02(1):23-30.
[11] Lagaros N D, Fragiadakis M. Fragility assessment of steel frames using neural networks [J]. Earthquake Spectra, 2007, 23(4): 735-752.
[12] Mitropoulou CC, Papadrakakis M. Developing fragility curves based on neural network IDA predictions [J]. Engineering Structure 2011;33(12):3409–3421.
[13] Pang Y, Dang X, Yuan W. An artificial neural network based method for seismic fragility analysis of highway bridges [J]. Advances in Structural Engineering, 2014,17(3): 413-428.
[14] Raghunandan M, Liel A B, Luco N. Aftershock collapse vulnerability assessment of reinforced concrete frame structures [J]. Earthquake Engineering & Structural Dynamics, 2015, 44(3): 419-439.
[15] Zhang Y, Burton H V, Sun H, et al. A machine learning framework for assessing post-earthquake structural safety [J]. Structural Safety, 2018, 72: 1-16.
[16] Mangalathu S, Heo G, Jeon J S. Artificial neural network based multi–dimensional fragility development of skewed concrete bridge classes[J]. Engineering Structures, 2018, 162: 166-176.
[17] Mangalathu S, Burton H V. Deep learning-based classification of earthquake impacted buildings using textual damage descriptions [J]. International Journal of Disaster Risk Reduction, 2019, 36.
[18] Mangalathu S, Hwang S H, Choi E, et al. Rapid seismic damage evaluation of bridge portfolios using machine learning techniques[J]. Engineering Structures, 2019, 201:109785.
[19] Xie Y, Zhang J, DesRoches R, et al. Seismic fragilities of single-column highway bridges with rocking column-footing [J]. Earthquake Engineering & Structural Dynamics, 2019, 48(7): 843-864.
[20] Sainct R, Feau C, Martinez J M, et al. Efficient methodology for seismic fragility curves estimation by active learning on Support Vector Machines[J]. Structural Safety, 2020, 86: 101972.
[21] Mangalathu S, Hwang S H, Jeon J S. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach [J]. Engineering Structures, 2020, 219: 110927.
[22] Feng D C, Liu Z T, Wang X D, et al. Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm [J]. Advanced Engineering Informatics, 2020, 45: 101126.
[23] 中华人民共和国行业标准. GB50011-2010建筑抗震设计规范[S]. 北京: 中国建筑工业出版社, 2010.
[24] 中华人民共和国建设部. GB50017-2003钢结构设计规范[S]. 北京: 中国建筑工业出版社, 2001.
[25] 杨超. 大型支撑-高层钢结构抗震性能分析[D]. 大连理工大学学位论文, 2011.
Analysis of Seismic Performances of Super Braced High-Rise Steel Structures [D]. Dissertation of Dalian University of Technology, 2011.
[26] 郑山锁, 石磊, 张晓辉, 等. 酸性大气环境下锈蚀钢框架结构振动台试验研究[J]. 工程力学, 2017, 34(11):77-88,108.
Zheng Shansuo, Shih Lei, Zhang Xiaohui, et al. Shaking table test of corroded steel frame structure under acidic atmosphere environment [J]. Engineering Mechanics, 2017, 34(11):77-88,108.
[27] Aha D W, Kibler D, Albert MK. Instance-based learning algorithms [J]. Machine learning, 1991, 6(1): 37-66.
[28] Cortes C, Vapnik V. Support-vector networks [J]. Machine learning, 1995, 20(3): 273-297.
[29] Quinlan J R. Induction of decision trees [J]. Machine learning, 1986, 1(1): 81-106.
[30] Breiman L. Random forests [J]. Machine learning, 2001, 45(1) :5-32.
[31] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting [J]. Journal of computer and system sciences, 1997, 55(1): 119-139.
[32] Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining [C]. 2016, 785-794.
[33] Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. SMOTE: synthetic minority over-sampling technique [J]. Journal of artificial intelligence research, 2002, 16: 321-357.
[34] Xu Y, Lu X, Tian Y, Huang Y. Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning. Journal of Earthquake Engineering, 2022;26(8):4259-4279.
[35] Riddell R. On ground motion intensity indices. Earthquake Spectra. 2007;23(1):147-173.