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[1]周 宇,骆 欢.基于特征向量信息支持向量机的RC框架易损性曲线预测[J].地震研究,2024,47(03):359-368.[doi:10.20015/j.cnki.ISSN1000-0666.2024.0052 ]
　ZHOU Yu,LUO Huan.Eigenvectors-informed Support Vector Machines for Fragility Curve Predictions of RC Frames[J].Journal of Seismological Research,2024,47(03):359-368.[doi:10.20015/j.cnki.ISSN1000-0666.2024.0052 ]
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基于特征向量信息支持向量机的RC框架易损性曲线预测(PDF/HTML)

《地震研究》[ISSN:1000-0666/CN:53-1062/P]

卷:
47
期数:
2024年03期
页码:
359-368
栏目:
人工智能
出版日期:
2024-05-01

文章信息/Info

Title:
Eigenvectors-informed Support Vector Machines for Fragility Curve Predictions of RC Frames
作者:
周 宇1; 2; 骆 欢1; 2
(1.湖北省地质灾害防治工程技术研究中心,湖北 宜昌 443002; 2.三峡大学 土木与建筑学院,湖北 宜昌 443002)
Author(s):
ZHOU Yu1; 2; LUO Huan1; 2
(1.Hubei Geological Disaster Prevention and Control Engineering Technology Research Center,Yichang 443002,Hubei,China;2.College of Civil Engineering & Architecture,China Three Gorges University,Yichang 443002,Hubei,China)
关键词:
钢筋混凝土框架;  易损性曲线;  特征向量;  支持向量机;  机器学习
Keywords:
RC frame structures;  fragility curves;  eigenvectors;  support vector machines;  machine learning
分类号:
TU973.2
DOI:
10.20015/j.cnki.ISSN1000-0666.2024.0052
摘要:
易损性曲线将结构破坏等级与地震动强度相关联,能够直观地反映结构破坏的概率,但在建立易损性曲线的过程中需要大量的结构非线性时程分析结果,因而计算效率不高。机器学习方法已被证明能较好地解决这一问题,但当训练数据的规模较大时,由于训练过程涉及求解大规模逆矩阵致使计算效率依然低下。为此,本文提出了一种特征向量信息支持向量机(EILS-SVM)的新方法克服此类方法的不足。在大规模数据集下,EILS-SVM能够筛选小规模子样本建立低秩核矩阵。这使得其训练过程只需求解小规模低秩矩阵的逆矩阵,进而极大提高计算效率。为了验证EILS-SVM的准确性和高效性,基于16500个钢筋混凝土(RC)框架在地震作用下的破坏数据,分别与支持向量机(LS-SVM)、随机森林、神经网络、线性判别分析(LDA)、贝叶斯作对比。结果表明,EILS-SVM 能准确预测 RC框架的易损性曲线,其计算效率最高能提升近27倍。
Abstract:
Fragility curves establish a correlation between structural damage levels and seismic intensity,offering an intuitive depiction of the probability of structural failure. However,the generation of these curves necessitates a substantial amount of structural nonlinear time-history analysis results,thereby rendering the computational process inefficient. Machine learning techniques have been proven to effectively address this issue,yet their efficacy diminishes with the increase in the scale of training data due to the computational demands of solving large-scale inverse matrices during the training phase. In response,this paper proposes a novel methodology,the Eigenvector Information-supported Support Vector Machine(EILS-SVM),which surmounts the limitations associated with these techniques. By employing a selective subsample to construct a low-rank kernel matrix in the context of large-scale datasets,the EILS-SVM method requires only the inversion of small-scale,low-rank matrices,significantly enhancing computational efficiency. To validate the accuracy and efficiency of the EILS-SVM,it is benchmarked against conventional models such as the Least Squares Support Vector Machine(LS-SVM),Random Forest,Neural Networks,Linear Discriminant Analysis(LDA),and Bayesian methods,using a dataset comprised of 16500 instances of damage in Reinforced Concrete(RC)frames subjected to seismic activities. The results indicate that the EILS-SVM is capable of accurately predicting the fragility curves of RC frames,with a computational efficiency improvement of up to 27 times compared to existing methodologies.

参考文献/References:


程诗焱,韩建平,于晓辉,等.2020.基于条带-云图法的地震动持时对结构地震易损性影响分析[J].地震工程与工程振动,40(5):107-117.
Cheng S Y,Han J P,Yu X H,et al.2020.Investigation on effect of ground motion duration on seismic fragility of structures based on stripe-cloud approach[J].Earthquake Engineering and Engineering Dynamics,40(5):107-117.(in Chinese)
李浩瑾,李俊杰,康飞.2013.基于LSSVM的重力坝地震稳定易损性分析[J].振动与冲击,32(4):146-152.
Li H J,Li J J,Kang F.2013.Analysis of seismic stability Fragility of gravity dam based on LSSVM[J].Journal of Vibration and Shock,32(4):146-152.(in Chinese)
路沙沙,徐红,张亚楠,等.2021.基于IDA方法的加固震损RC框架结构地震易损性分析[J].地震研究,44(4):673-681.
Lu S S,Xu H,Zhang Y N,et al.2021.Seismic fragility analysis of reinforced RC frame structure with seismic damage based on incremental dynamic analysis[J].Journal of Seismological Research,44(4):673-681.(in Chinese)
吕大刚,于晓辉,陈志恒.2011.钢筋混凝土框架结构侧向倒塌地震易损性分析[J].哈尔滨工业大学学报,43(6):1-5.
Lyu D G,Yu X H,Cheng Z H.2011.Lateral seismic collapse fragility analysis of RC frame structures[J].Journal of Harbin Institute of Technology,43(6):1-5.(in Chinese)
孙柏涛,张桂欣.2012.汶川8.0级地震中各类建筑结构地震易损性统计分析[J].土木工程学报,45(5):26-30.
Sun B T,Zhang G X.2012.Statistical analysis of seismic vulnerability of various building structures in Wenchuan M8.0 earthquake[J].China Civil Engineering Journal,45(5):26-30.(in Chinese)
薛成凤,刘来君,武芳文.2017.大跨度混凝土梁斜拉桥横向地震作用易损性分析[J].地震研究,40(1):65-69.
Xue C F,Liu L J,Wu F W.2017.Fragility analysis of long-span concrete cable-stayed bridge under lateral seismic loads[J].Journal of Seismological Research,40(1):65-69.(in Chinese)
于晓辉,吕大刚.2016.基于云图-条带法的概率地震需求分析与地震易损性分析[J].工程力学,33(6):68-76.
Yu X H,Lyu D G.2016.Probabilistic seismic demand analysis and seismic fragility analysis based on a cloud-stripe method[j].Engineering Mechanics,33(6):68-76.(in Chinese)
GB 50011—2010.建筑抗震设计规范[S].
GB 50011—2010.Seismic design of buildings[S].(in Chinese)
Baker J W.2015.Efficient analytical fragility function fitting using dynamic structural analysis[J].Earthquake Spectra,31(1):579-599.
Bradley B A.2010.A generalized conditional intensity measure approach and holistic ground-motion selection[J].Earthquake Engineering & Structural Dynamics,39(12):1321-1342.
Cervantes J,Garcia-Lamont F,Rodríguez-Mazahua L,et al.2020.A comprehensive survey on support vector machine classification:Applications,challenges and trends[J].Neurocomputing,408:189-215.
Charless F,Serge B,Fan C,et al.2019.Spectral grouping using the Nystrom method[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,26(2):214-225.
Fatimah S,Wong J.2021.Sensitivity of the fragility curve on type of analysis methods,applied ground motions and their selection techniques[J].International Journal of Steel Structures,21(4):1292-1304.
Gentile R,Galasso C.2020.Gaussian process regression for seismic fragility assessment of building portfolios[J].Structural Safety,87:101980.
Girolami M.2002.Mercer kernel-based clustering in feature space[J].IEEE Transactions on Neural Networks,13(3):780-784.
Jalayer F,Ebrahimian H,Miano A,et al.2017.Analytical fragility assessment using unscaled ground motion records[J].Earthquake Engineering & Structural Dynamics,46(15):2639-2663.
James G,Witten D,Hastie T,et al.2013.An introduction to statistical learning:with applications in R[M].Berlin:Springer Group.
Kiani J,Camp C,Pezeshk S.2019.On the application of machine learning techniques to derive seismic fragility curves[J].Computers & Structures,218:108-122.
Lallemant D,Kiremidjian A,Burton H.2015.Statistical procedures for developing earthquake damage fragility curves[J].Earthquake Engineering & Structural Dynamics,44(9):1373-1389.
Mahmoudi S N,Chouinard L.2016.Seismic fragility assessment of highway bridges using support vector machines[J].Bulletin of Earthquake Engineering,14(6):1571-1587.
Mattei S,Bedon C.2021.Analytical Fragility Curves for Seismic Design of Glass Systems Based on Cloud Analysis[J].Symmetry,13(8):1541.
Mazzoni S,Mckenna F,Scott M H,et al.2006.Open system for earthquake engineering simulation user command language manual[M/OL].University of California,Berkeley.(2023-07-19)[2023-08-20].http://opensees.berkeley.edu.
Mitropoulou C C,Papadrakakis M.2011.Developing fragility curves based on neural network IDA predictions[J].Engineering Structures,33(12):3409-3421.
Pujari N N,Mandal T K,Ghosh S,et al.2013.Optimization of IDA-based fragility curves[C]//safety,reliability,risk and life-cycle performance of structures and infrastructures,proceedings of the 11th international conference on structural safety and reliability,New York,USA:16-20.
Sainct R,Feau C,Martinez J M,et al.2020.Efficient methodology for seismic fragility curves estimation by active learning on Support Vector Machines[J].Structural Safety,86:101972.
Suykens J A K,Lukas L,Vandewalle J.2000.Sparse approximation using least squares support vector machines[C]// IEEE,2000 IEEE international symposium on circuits and systems(ISCAS),2:757-760.
Suykens J A K,Vandewalle J.1999.Least squares support vector machine classifiers[J].Neural processing letters,9(3):293-300.
Vamvatsikos D,Cornell C A.2002.Incremental dynamic analysis[J].earthquake engineering and structural dynamics,31(3):491-514.
Wang W K,Luo H.2023.A dataset of reinforced concrete frame response under ground motions based on OpenSees simulations[DB/OL].Harvard Dataverse.(2023-10-10)[2023-10-15].https://doi.org/10.7910/DVN/HDBJ0E.

备注/Memo

备注/Memo:
收稿日期:2023-09-19.
基金项目:湖北省自然科学基金面上项目(2022CFB294); 国家自然科学基金青年科学项目(52208485).
第一作者简介:周 宇(1997-),硕士研究生在读,主要从事结构抗震与机器学习交叉研究.E-mail:891363206@qq.com.
通信作者简介:骆 欢(1988-),博士,副教授,主要从事结构抗震与机器学习交叉研究.E-mail:hluo@ctgu.edu.cn.
更新日期/Last Update: 2024-05-01