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Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Ioannis P. Mitseas
  • Ioannis A. Kougioumtzoglou
  • Michael Beer
  • Edoardo Patelli

External Research Organisations

  • University of Liverpool
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  • Citations
    • Citation Indexes: 10
  • Captures
    • Readers: 14
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Details

Original languageEnglish
Title of host publicationVulnerability, Uncertainty, and Risk
Subtitle of host publicationQuantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014
EditorsJim W. Hall, Siu-Kui Au, Michael Beer
PublisherAmerican Society of Civil Engineers (ASCE)
Pages215-224
Number of pages10
ISBN (electronic)9780784413609
Publication statusPublished - 2014
Externally publishedYes
Event2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 - Liverpool, United Kingdom (UK)
Duration: 13 Jul 201416 Jul 2014

Abstract

An efficient robust design optimization (RBO) framework is developed for linear multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic earthquake excitations. A significant feature of the developed RBO framework relates to the consideration of both inter-storey drift and floor acceleration second-order statistics as performance measures. Further, an efficient frequency domain approach is utilized for determining the system response Evolutionary Power Spectrum (EPS) matrix circumventing computationally intensive Monte Carlo simulations. Furthermore, the optimization problem is solved by employing a Genetic Algorithm based approach. An illustrative numerical example is included to demonstrate the efficiency and robustness of the proposed framework.

ASJC Scopus subject areas

Cite this

Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation. / Mitseas, Ioannis P.; Kougioumtzoglou, Ioannis A.; Beer, Michael et al.
Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. ed. / Jim W. Hall; Siu-Kui Au; Michael Beer. American Society of Civil Engineers (ASCE), 2014. p. 215-224.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Mitseas, IP, Kougioumtzoglou, IA, Beer, M, Patelli, E & Mottershead, JE 2014, Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation. in JW Hall, S-K Au & M Beer (eds), Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. American Society of Civil Engineers (ASCE), pp. 215-224, 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014, Liverpool, United Kingdom (UK), 13 Jul 2014. https://doi.org/10.1061/9780784413609.022
Mitseas, I. P., Kougioumtzoglou, I. A., Beer, M., Patelli, E., & Mottershead, J. E. (2014). Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation. In J. W. Hall, S.-K. Au, & M. Beer (Eds.), Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 (pp. 215-224). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784413609.022
Mitseas IP, Kougioumtzoglou IA, Beer M, Patelli E, Mottershead JE. Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation. In Hall JW, Au SK, Beer M, editors, Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. American Society of Civil Engineers (ASCE). 2014. p. 215-224 Epub 2014 Jul 7. doi: 10.1061/9780784413609.022
Mitseas, Ioannis P. ; Kougioumtzoglou, Ioannis A. ; Beer, Michael et al. / Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation. Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. editor / Jim W. Hall ; Siu-Kui Au ; Michael Beer. American Society of Civil Engineers (ASCE), 2014. pp. 215-224
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title = "Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation",
abstract = "An efficient robust design optimization (RBO) framework is developed for linear multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic earthquake excitations. A significant feature of the developed RBO framework relates to the consideration of both inter-storey drift and floor acceleration second-order statistics as performance measures. Further, an efficient frequency domain approach is utilized for determining the system response Evolutionary Power Spectrum (EPS) matrix circumventing computationally intensive Monte Carlo simulations. Furthermore, the optimization problem is solved by employing a Genetic Algorithm based approach. An illustrative numerical example is included to demonstrate the efficiency and robustness of the proposed framework.",
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