Efficient methodology for seismic fragility curves estimation by active learning on Support Vector Machines
Tài liệu tham khảo
Kennedy, 1980, Probabilistic seismic safety study of an existing nuclear power plant, Nucl Eng Des, 59, 315, 10.1016/0029-5493(80)90203-4
Ghobarah, 2001, Performance-based design in earthquake engineering: state of development, Eng Struct, 23, 878, 10.1016/S0141-0296(01)00036-0
Noh, 2014, Development of empirical and analytical fragility functions using kernel smoothing methods, Earthquake Eng Struct Dyn, 44, 1163, 10.1002/eqe.2505
Zhang, 2009, Evaluating effectiveness and optimum design of isolation devices for highway bridges using the fragility function method, Eng Struct, 31, 1648, 10.1016/j.engstruct.2009.02.017
Saha, 2016, Uncertainty quantification and seismic fragility of base-isolated liquid storage tanks using response surface models, Probab Eng Mech, 43, 20, 10.1016/j.probengmech.2015.10.008
Patil, 2016, Structural performance of a parked wind turbine tower subjected to strong ground motions, Eng Struct, 120, 92, 10.1016/j.engstruct.2016.04.020
Gidaris, 2015, Kriging metamodeling in seismic risk assessment based on stochastic ground motion models, Earthquake Eng Struct Dyn, 44, 2377, 10.1002/eqe.2586
Kameshwar, 2018, Storm surge fragility assessment of above ground storage tanks, Struct Saf, 70, 48, 10.1016/j.strusafe.2017.10.002
Wang, 2020, Influence of input motion’s control point location in nonlinear SSI analysis of equipment seismic fragilities: case study on the Kashiwazaki-Kariwa NPP, Pure Appl Geophys, 10.1007/s00024-020-02467-3
Sez, 2011, Effect of the inelastic dynamic soil-structure interaction on the seismic vulnerability assessment, Struct Saf, 33, 51, 10.1016/j.strusafe.2010.05.004
Mathey, 2018, Experimental and numerical analyses of variability in the responses of imperfect slender free rigid blocks under random dynamic excitations, Eng Struct, 172, 891, 10.1016/j.engstruct.2018.06.064
Quilligan, 2012, Fragility analysis of steel and concrete wind turbine towers, Eng Struct, 36, 270, 10.1016/j.engstruct.2011.12.013
Ellingwood, 2009, Quantifying and communicating uncertainty in seismic risk assessment, Struct Saf, 31, 179, 10.1016/j.strusafe.2008.06.001
Der Kiureghian, 2009, Aleatory or epistemic? does it matter?, Struct Saf, 31, 105, 10.1016/j.strusafe.2008.06.020
Masanobu Shinozuka, 2000, Statistical analysis of fragility curves, J Eng Mech, 126, 1224, 10.1061/(ASCE)0733-9399(2000)126:12(1224)
Baker, 2015, Efficient analytical fragility function fitting using dynamic structural analysis, Earthquake Spectra, 31, 579, 10.1193/021113EQS025M
Silva, 2016, Exploring risk-targeted hazard maps for Europe, Earthquake Spectra, 32, 1165, 10.1193/112514eqs198m
Mandal, 2016, Seismic fragility analysis of a typical indian PHWR containment: comparison of fragility models, Struct Saf, 58, 11, 10.1016/j.strusafe.2015.08.003
Hariri-Ardebili, 2016, Probabilistic seismic demand model and optimal intensity measure for concrete dams, Struct Saf, 59, 67, 10.1016/j.strusafe.2015.12.001
Zentner, 2010, Numerical computation of fragility curves for NPP equipment, Nucl Eng Des, 240, 1614, 10.1016/j.nucengdes.2010.02.030
Mai, 2017, Seismic fragility curves for structures using non-parametric representations, Front Struct Civil Eng, 11, 169, 10.1007/s11709-017-0385-y
Zentner, 2017, A general framework for the estimation of analytical fragility functions based on multivariate probability distributions, Struct Saf, 64, 54, 10.1016/j.strusafe.2016.09.003
Trevlopoulos, 2019, Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering, Struct Saf, 81, 10.1016/j.strusafe.2019.05.002
Park, 2014, Rapid seismic damage assessment of railway bridges using the response-surface statistical model, Struct Saf, 47, 1, 10.1016/j.strusafe.2013.10.001
Seo, 2013, Use of response surface metamodels to generate system level fragilities for existing curved steel bridges, Eng Struct, 52, 642, 10.1016/j.engstruct.2013.03.023
Wang, 2018, Seismic fragility analysis with artificial neural networks: application to nuclear power plant equipment, Eng Struct, 162, 213, 10.1016/j.engstruct.2018.02.024
Luco, 2007, Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions, Earthquake Spectra, 23, 357, 10.1193/1.2723158
Mackie, 2001, Probabilistic seismic demand model for california highway bridges, J Bridge Eng, 6, 468, 10.1061/(ASCE)1084-0702(2001)6:6(468)
Paolo Giovenale, 2004, Comparing the adequacy of alternative ground motion intensity measures for the estimation of structural responses, Earthquake Eng Struct Dyn, 33, 951, 10.1002/eqe.386
Baker, 2008, Vector-valued intensity measures incorporating spectral shape for prediction of structural response, J Earthquake Eng, 12, 534, 10.1080/13632460701673076
Padgett, 2008, Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios, Earthquake Eng Struct Dyn, 37, 711, 10.1002/eqe.782
Hasenjäger, 2002, Active learning in neural networks, 137
Seung, 1992, ’92, 287
Gazut, 2008, Towards the optimal design of numerical experiments, Trans Neur Netw, 19, 874, 10.1109/TNN.2007.915111
Tong, 2002, Support vector machine active learning with applications to text classification, J Mach Learn Res, 2, 45
Rezaeian, 2008, A stochastic ground motion model with separable temporal and spectral nonstationarities, Earthquake Eng Struct Dyn, 37, 1565, 10.1002/eqe.831
Sabetta, 1996, Estimation of response spectra and simulation of nonstationary earthquake ground motions, Bull Seismol Soc Am, 86, 337, 10.1785/BSSA0860020337
Levy, 1976, Generation of artificial time-histories, rich in all frequencies, from given response spectra, Nucl Eng Des, 38, 241, 10.1016/0029-5493(76)90099-6
Pousse, 2006, Nonstationary stochastic simulation of strong ground motion time histories including natural variability: application to the k-net japanese database, Bull Seismol Soc Am, 96, 2103, 10.1785/0120050134
Zentner, 2012, Enrichment of seismic ground motion databases using karhunen-loève expansion, Earthquake Eng Struct Dyn, 41, 1945, 10.1002/eqe.2166
Rezaeian, 2010, Simulation of synthetic ground motions for specified earthquake and site characteristics, Earthquake Eng Struct Dyn, 39, 1155
Simon Kwong, 2016, Evaluation of the exact conditional spectrum and generalized conditional intensity measure methods for ground motion selection, Earthquake Eng Struct Dyn, 45, 757, 10.1002/eqe.2683
Bachmann, 2018, Is rocking motion predictable?, Earthquake Eng Struct Dyn, 47, 535, 10.1002/eqe.2978
Tsioulou, 2018, Modification of stochastic ground motion models for matching target intensity measures, Earthquake Eng Struct Dyn, 47, 3, 10.1002/eqe.2933
Vassiliou, 2017, The three-dimensional behavior of inverted pendulum cylindrical structures during earthquakes, Earthquake Eng Struct Dyn, 46, 2261, 10.1002/eqe.2903
Sanaz, 2010
Ambraseys NN, Smit P, Berardi R, Rinaldis D, Cotton F, Berge C. Dissemination of european strongmotion data, 2000. CD-ROM collection. European Commission, Directorate-General XII, Environmental and Climate Programme, ENV4-CT97-0397, Brussels, Belgium.
Kristan, 2011, Multivariate online kernel density estimation with gaussian kernels, Pattern Recogn, 44, 2630, 10.1016/j.patcog.2011.03.019
Kremer, 2014, Active learning with support vector machines, Wiley Int Rev Data Min Knowl Disc, 4, 313, 10.1002/widm.1132