202
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Seismic Reliability Analysis of Structures by an Adaptive Support Vector Regression-Based Metamodel

&
Pages 1590-1614 | Received 09 May 2022, Accepted 27 Jul 2023, Published online: 11 Aug 2023

References

  • Afshari, S. S., F. Enayatollahi, X. Xu, and X. Liang. 2022. Machine learning-based methods in structural reliability analysis: A review. Reliability Engineering & System Safety 219 (March):108223. doi:10.1016/J.RESS.2021.108223.
  • Bichon, B. J., M. S. Eldred, L. P. Swiler, S. Mahadevan, and J. M. McFarland. 2008. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA Journal 46 (10):2459–68. doi:10.2514/1.34321.
  • Bourinet, J. M. 2016. Rare-event probability estimation with adaptive support vector regression surrogates. Reliability Engineering and System Safety 150 (June):210–21. doi:10.1016/j.ress.2016.01.023.
  • Bucher, C. G., and U. Bourgund. 1990. A fast and efficient response surface approach for structural reliability problems. Structural Safety 7 (1):57–66. doi:10.1016/0167-4730(90)90012-E.
  • Buratti, N., B. Ferracuti, and M. Savoia. 2010. Response surface with random factors for seismic fragility of reinforced concrete frames. Structural Safety 32 (1):42–51. doi:10.1016/J.STRUSAFE.2009.06.003.
  • Cheng, K., and Z. Lu. 2020. Structural reliability analysis based on ensemble learning of surrogate models. Structural Safety 83 (March):101905. doi:10.1016/j.strusafe.2019.101905.
  • Dai, H., B. Zhang, and W. Wang. 2015. A multiwavelet support vector regression method for efficient reliability assessment. Reliability Engineering and System Safety 136 (April):132–39. doi:10.1016/j.ress.2014.12.002.
  • Dai, H., H. Zhang, W. Wang, and G. Xue. 2012. Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression. Computer-Aided Civil and Infrastructure Engineering 27 (9):676–86. doi:10.1111/j.1467-8667.2012.00767.x.
  • Echard, B., N. Gayton, and M. Lemaire. 2011. AK-MCS: An active learning reliability method combining kriging and monte carlo simulation. Structural Safety 33 (2):145–54. doi:10.1016/j.strusafe.2011.01.002.
  • Fang, K.-T., D. K. J. Lin, P. Winker, and Y. Zhang. 2000. Uniform design: Theory and application. Technometrics 42 (3):237–48. doi:10.1080/00401706.2000.10486045.
  • FEMA-356. 2000. Prestandard and commentary for the seismic rehabilitation of buildings. Washington, DC: Federal Emergency Management Agency (FEMA).
  • Franchin, P., A. Lupoi, P. E. Pinto, and M. Ij Schotanus. 2003. Seismic fragility of reinforced concrete structures using a response surface approach. Journal of Earthquake Engineering 7 (SPEC. 1):45–77. doi:10.1080/13632460309350473.
  • Gaxiola-Camacho, J. R., H. Azizsoltani, F. J. Villegas-Mercado, and A. Haldar. 2017. A novel reliability technique for implementation of performance-based seismic design of structures. Engineering Structures 142 (July):137–47. doi:10.1016/J.ENGSTRUCT.2017.03.076.
  • Gehl, P., and D. D’Ayala. 2016. Development of bayesian networks for the multi-hazard fragility assessment of bridge systems. Structural Safety 60 (May):37–46. doi:10.1016/J.STRUSAFE.2016.01.006.
  • Ghosh, S. 2020. Seismic Fragility Analysis of Structures with Special Emphasis to Northeast India. PhD thesis, Shibpur: Indian Institute of Engineering Science and Technology.
  • Ghosh, S., and S. Chakraborty. 2017. Simulation based improved seismic fragility analysis of structures. Earthquakes and Structures 12 (5):569–81. doi:10.12989/EAS.2017.12.5.569.
  • Ghosh, S., S. Ghosh, and S. Chakraborty. 2018. Seismic Reliability analysis of reinforced concrete bridge pier using efficient response surface method–based simulation. Advances in Structural Engineering 21 (15):2326–39. doi:10.1177/1369433218773422.
  • Ghosh, J., J. E. Padgett, and L. Dueñas-Osorio. 2013. Surrogate modeling and failure surface visualization for efficient seismic vulnerability assessment of highway bridges. Probabilistic Engineering Mechanics 34 (October):189–99. doi:10.1016/J.PROBENGMECH.2013.09.003.
  • Ghosh, S., A. Roy, and S. Chakraborty. 2018. Support vector regression based metamodeling for seismic reliability analysis of structures. Applied Mathematical Modelling 64 (December):584–602. doi:10.1016/j.apm.2018.07.054.
  • Ghosh, S., A. Roy, and S. Chakraborty. 2021. Kriging metamodeling-based monte carlo simulation for improved seismic fragility analysis of structures. Journal of Earthquake Engineering 25 (7):1316–36. doi:10.1080/13632469.2019.1570395.
  • Gidaris, I., A. A. Taflanidis, and G. P. Mavroeidis. 2015. Kriging metamodeling in seismic risk assessment based on stochastic ground motion models. Earthquake Engineering & Structural Dynamics 44 (14):2377–99. doi:10.1002/EQE.2586.
  • Günay, S., and K. M. Mosalam. 2013. PEER performance-based earthquake engineering methodology, revisited. Journal of Earthquake Engineering 17 (6):829–58. doi:10.1080/13632469.2013.787377.
  • Hurtado, J. E. 2004. An examination of methods for approximating implicit limit state functions from the viewpoint of statistical learning theory. Structural Safety 26 (3):271–93. doi:10.1016/j.strusafe.2003.05.002.
  • Hurtado, J. E. 2007. Filtered importance sampling with support vector margin: A powerful method for structural reliability analysis. Structural Safety 29 (1):2–15. doi:10.1016/j.strusafe.2005.12.002.
  • Jeddi, A. B., A. Shafieezadeh, J. Hur, J. G. Ha, D. Hahm, and M. K. Kim. 2022. Multi-Hazard typhoon and earthquake collapse fragility models for transmission towers: An active learning reliability approach using gradient boosting classifiers. Earthquake Engineering & Structural Dynamics 51 (15):3552–73. doi:10.1002/EQE.3735.
  • Johnson, M. E., L. M. Moore, and D. Ylvisaker. 1990. Minimax and maximin distance designs. Journal of Statistical Planning and Inference 26 (2):131–48. doi:10.1016/0378-3758(90)90122-B.
  • Jones, D. R., M. Schonlau, and W. J. Welch. 1998. Efficient global optimization of expensive black-box functions. Journal of Global Optimization 13 (4):455–92. doi:10.1023/A:1008306431147.
  • Keshtegar, B., M. E. A. B. Seghier, E. Zio, J. A. F. O. Correia, S. P. Zhu, and N. Thoi Trung. 2021. Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression. Computer Methods in Applied Mechanics and Engineering 381 (August):113818. doi:10.1016/J.CMA.2021.113818.
  • Kim, S. H., and M. Q. Feng. 2003. Fragility analysis of bridges under ground motion with spatial variation. International Journal of Non-Linear Mechanics 38 (5):705–21. doi:10.1016/S0020-7462(01)00128-7.
  • Kundu, A., S. Ghosh, and S. Chakraborty. 2022. A long short-term memory based deep learning algorithm for seismic response uncertainty quantification. Probabilistic Engineering Mechanics 67 (January):103189. doi:10.1016/j.probengmech.2021.103189.
  • Kwon, O. S., and A. Elnashai. 2006. The effect of material and ground motion uncertainty on the seismic vulnerability curves of RC structure. Engineering Structures 28 (2):289–303. doi:10.1016/J.ENGSTRUCT.2005.07.010.
  • Lagaros, N. D., and M. Fragiadakis. 2007. Fragility assessment of steel frames using neural networks. Earthquake Spectra 23 (4):735–52. doi:10.1193/1.2798241.
  • Lagaros, N. D., Y. Tsompanakis, P. N. Psarropoulos, and E. C. Georgopoulos. 2009. Computationally efficient seismic fragility analysis of geostructures. Computers and Structures 87 (19–20):1195–203. doi:10.1016/j.compstruc.2008.12.001.
  • Li, X., C. Gong, L. Gu, W. Gao, Z. Jing, and H. Su. 2018. A sequential surrogate method for reliability analysis based on radial basis function. Structural Safety 73 (July):42–53. doi:10.1016/j.strusafe.2018.02.005.
  • Li, H.-S. S., Z.-Z. Lü, Z.-F. F. Yue, Z. Z. Lu, and Z.-F. F. Yue. 2006. Support vector machine for structural reliability analysis. Applied Mathematics and Mechanics 27 (10):1295–303. doi:10.1007/s10483-006-1001-z.
  • Lin, D. K. J., and W. Tu. 1995. Dual response surface optimization. Journal of Quality Technology 27 (1):34–39. doi:10.1080/00224065.1995.11979556.
  • Mai, C. V., M. D. Spiridonakos, E. N. Chatzi, and B. Sudret. 2016. Surrogate modelling for stochastic dynamical systems by combining narx models and polynomial chaos expansions. International Journal for Uncertainty Quantification 6 (4):313–39. doi:10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2016016603.
  • Mangalathu, S., J. S. Jeon, and R. DesRoches. 2018. Critical uncertainty parameters influencing seismic performance of bridges using lasso regression. Earthquake Engineering & Structural Dynamics 47 (3):784–801. doi:10.1002/EQE.2991.
  • Marelli, S., and B. Sudret. 2018. An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis. Structural Safety 75 (November):67–74. doi:10.1016/j.strusafe.2018.06.003.
  • McKenna, F., G. L. Fenves, M. H. Scott, and B. Jeremic. 2016. OpenSees: Open system for earthquake engineering simulation. Berkeley, CA: Pacific Earthquake Engineering Research Center, University of California.
  • Möller, O., R. O. Foschi, M. Rubinstein, and L. Quiroz. 2009. Seismic structural reliability using different nonlinear dynamic response surface approximations. Structural Safety 31 (5):432–42. doi:10.1016/J.STRUSAFE.2008.12.001.
  • Moura, M. D. C., E. Zio, I. D. Lins, and E. Droguett. 2011. Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety 96 (11):1527–34. doi:10.1016/J.RESS.2011.06.006.
  • Moustapha, M., S. Marelli, and B. Sudret. 2022. Active learning for structural reliability: Survey, general framework and benchmark. Structural Safety 96 (May):102174. doi:10.1016/J.STRUSAFE.2021.102174.
  • Pan, Q., and D. Dias. 2017. An efficient reliability method combining adaptive support vector machine and monte carlo simulation. Structural Safety 67 (July):85–95. doi:10.1016/j.strusafe.2017.04.006.
  • Park, J., and P. Towashiraporn. 2014. Rapid seismic damage assessment of railway bridges using the response-surface statistical model. Structural Safety 47 (March):1–12. doi:10.1016/J.STRUSAFE.2013.10.001.
  • Pinto, P. E. 2001. Reliability methods in earthquake engineering. Progress in Structural Engineering and Materials 3 (1):76–85. doi:10.1002/PSE.64.
  • Porter, K., R. Kennedy, and R. Bachman. 2007. Creating fragility functions for performance-based earthquake engineering. Earthquake Spectra 23 (2):471–89. doi:10.1193/1.2720892.
  • Rajashekhar, M. R., and B. R. Ellingwood. 1993. A new look at the response surface approach for reliability analysis. Structural Safety 12 (3):205–20. doi:10.1016/0167-4730(93)90003-J.
  • Ren, C., Y. Aoues, D. Lemosse, and E. S. De Cursi. 2022. Ensemble of surrogates combining kriging and artificial neural networks for reliability analysis with local goodness measurement. Structural Safety 96 (January):102186. doi:10.1016/j.strusafe.2022.102186.
  • Richard, B., C. Cremona, and L. Adelaide. 2012. A response surface method based on support vector machines trained with an adaptive experimental design. Structural Safety 39 (November):14–21. doi:10.1016/j.strusafe.2012.05.001.
  • Rocco, C. M., and J. Alí Moreno. 2002. Fast monte carlo reliability evaluation using support vector machine. Reliability Engineering and System Safety 76 (3):237–43. doi:10.1016/S0951-8320(02)00015-7.
  • Roy, A., and S. Chakraborty. 2020. Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures. Reliability Engineering and System Safety 200 (August):106948. doi:10.1016/j.ress.2020.106948.
  • Roy, A., and S. Chakraborty. 2022. Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model. Reliability Engineering and System Safety 219 (March):108260. doi:10.1016/j.ress.2021.108260.
  • Roy, A., and S. Chakraborty. 2023. Support vector machine in structural reliability analysis: A review. Reliability Engineering & System Safety 233 (May):109126. doi:10.1016/j.ress.2023.109126.
  • Roy, A., S. Chakraborty, and S. Adhikari. 2023. Reliability analysis of structures by active learning enhanced sparse bayesian regression. Journal of Engineering Mechanics 149 (5):04023024. doi:10.1061/jenmdt.emeng-6964.
  • Roy, A., R. Manna, and S. Chakraborty. 2019. Support vector regression based metamodeling for structural reliability analysis. Probabilistic Engineering Mechanics 55 (January):78–89. doi:10.1016/j.probengmech.2018.11.001.
  • Saha, S. K., V. Matsagar, and S. Chakraborty. 2016. Uncertainty quantification and seismic fragility of base-isolated liquid storage tanks using response surface models. Probabilistic Engineering Mechanics 43 (January):20–35. doi:10.1016/J.PROBENGMECH.2015.10.008.
  • Sainct, R., C. Feau, J. M. Martinez, and J. Garnier. 2020. Efficient methodology for seismic fragility curves estimation by active learning on support vector machines. Structural Safety 86 (September):101972. doi:10.1016/j.strusafe.2020.101972.
  • Santner, T. J., B. J. Williams, and W. I. Notz. 2003. Space-filling designs for computer experiments 121–61. Springer: New York, NY. doi:10.1007/978-1-4757-3799-8_5.
  • Schölkopf, B., C. J. C. Burges, and A. J. Smola. 1999. Advances in kernel methods : Support vector learning. MIT Press. https://dl.acm.org/citation.cfm?id=299094.
  • Segura, R., J. E. Padgett, and P. Paultre. 2020. Metamodel-based seismic fragility analysis of concrete gravity dams. Journal of Structural Engineering 146 (7):04020121. doi:10.1061/(ASCE)ST.1943-541X.0002629.
  • Seo, J., L. Dueñas-Osorio, J. I. Craig, and B. J. Goodno. 2012. Metamodel-based regional vulnerability estimate of irregular steel moment-frame structures subjected to earthquake events. Engineering Structures 45 (December):585–97. doi:10.1016/J.ENGSTRUCT.2012.07.003.
  • Seo, J., and D. G. Linzell. 2013. Use of response surface metamodels to generate system level fragilities for existing curved steel bridges. Engineering Structures 52 (July):642–53. doi:10.1016/J.ENGSTRUCT.2013.03.023.
  • Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (3):199–222. doi:10.1023/B:STCO.0000035301.49549.88.
  • Teixeira, R., M. Nogal, and A. O’Connor. 2021. Adaptive approaches in metamodel-based reliability analysis: A review. Structural Safety 89 (March):102019. doi:10.1016/j.strusafe.2020.102019.
  • Towashiraporn, P. 2004. Building seismic fragilities using response surface metamodels. Georgia Institute of Technology. Georgia Institute of Technology, Georgia Institute of Technology. https://search.proquest.com/openview/c9295fb2060f2854f76a5457a5db79b0/1?pq-origsite=gscholar&cbl=18750&diss=y.
  • Unnikrishnan, V. U., A. M. Prasad, and B. N. Rao. 2013. Development of fragility curves using high-dimensional model representation. Earthquake Engineering & Structural Dynamics 42 (3):419–30. doi:10.1002/EQE.2214.
  • Vapnik, V. N. 1995. The nature of statistical learning theory. Springer New York. doi:10.1007/978-1-4757-2440-0.
  • Vapnik, V. N. 1998. Statistical learning theory. New York: Wiley.
  • Vapnik, V. N. 2000. The nature of statistical learning theory. Springer New York. doi:10.1007/978-1-4757-3264-1.
  • Wang, Z., N. Pedroni, I. Zentner, and E. Zio. 2018. Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment. Engineering Structures 162 (May):213–25. doi:10.1016/J.ENGSTRUCT.2018.02.024.
  • Wong, S. M., R. E. Hobbs, and C. Onof. 2005. An adaptive response surface method for reliability analysis of structures with multiple loading sequences. Structural Safety 27 (4):287–308. doi:10.1016/j.strusafe.2005.02.001.
  • Xiao, Y., K. Ye, and W. He. 2020. An improved response surface method for fragility analysis of base-isolated structures considering the correlation of seismic demands on structural components. Bulletin of Earthquake Engineering 18 (8):4039–59. doi:10.1007/s10518-020-00836-w.
  • Xiao, Y., F. Yue, X. Zhang, and S. Carbonari. 2021. Seismic fragility analysis of structures based on adaptive gaussian process regression metamodel. Shock and Vibration 2021:1–16. doi:10.1155/2021/7622130.
  • Xiao, Y., X. Zhang, F. Yue, M. M. Shahzad, X. Wang, and B. Fan. 2022. Seismic fragility analysis of mega-frame with vibration control substructure based on dual surrogate model and active Learning. Buildings 12 (6):752. doi:10.3390/BUILDINGS12060752.
  • Xiao, N. C., M. J. Zuo, and C. Zhou. 2018. A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis. Reliability Engineering and System Safety 169 (January):330–38. doi:10.1016/j.ress.2017.09.008.
  • Xu, Z., and J. H. Saleh. 2021. Machine Learning for Reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering & System Safety 211:107530. doi:https://doi.org/10.1016/j.ress.2021.107530. August 2020.
  • Zentner, I., and E. Borgonovo. 2014. Construction of variance-based metamodels for probabilistic seismic analysis and fragility assessment. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 8 (3):202–16. doi:10.1080/17499518.2014.958173.
  • Zhang, Y., and G. Wu. 2019. Seismic vulnerability analysis of rc bridges based on kriging model. Journal of Earthquake Engineering 23 (2):242–60. doi:10.1080/13632469.2017.1323040.
  • Zhou, T., Y. Peng, and J. Li. 2019. An efficient reliability method combining adaptive global metamodel and probability density evolution method. Mechanical Systems and Signal Processing 131 (September):592–616. doi:10.1016/J.YMSSP.2019.06.009.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.