Details
| Original language | English |
|---|---|
| Number of pages | 8 |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 - Vancouver, Canada Duration: 12 Jul 2015 → 15 Jul 2015 |
Conference
| Conference | 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 |
|---|---|
| Country/Territory | Canada |
| City | Vancouver |
| Period | 12 Jul 2015 → 15 Jul 2015 |
Abstract
A performance-based multi-objective design optimization framework for nonlinear/hysteretic multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic excitation is formulated. The core of the developed framework is an efficient approximate dimension reduction technique based on the concepts of statistical linearization and of stochastic averaging for determining the non-stationary system response amplitude probability density functions (PDFs); thus, computationally intensive Monte Carlo simulations are circumvented. Note that the approach can handle readily stochastic excitations of arbitrary non-separable evolutionary power spectrum (EPS) forms that exhibit strong variability in both the intensity and the frequency content. Further, approximate closed-form expressions are derived for the non-stationary inter-story drift ratio amplitude PDFs corresponding to each and every DOF. In this regard, considering appropriately defined damage measures structural system related fragility curves are determined at a low computational cost as well. Finally, the structural system design optimization problem is formulated as a multi-objective one to be solved by a Genetic Algorithm based approach. A building structure comprising the versatile Bouc-Wen (hysteretic) model serves as an example for demonstrating the efficiency of the methodology.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Mathematics(all)
- Statistics and Probability
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2015. Paper presented at 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012, Vancouver, Canada.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Nonlinear stochastic dynamic analysis for performance based multi-objective optimum design considering life cycle seismic loss estimation
AU - Mitseas, Ioannis P.
AU - Kougioumtzoglou, Ioannis A.
AU - Beer, Michael
PY - 2015
Y1 - 2015
N2 - A performance-based multi-objective design optimization framework for nonlinear/hysteretic multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic excitation is formulated. The core of the developed framework is an efficient approximate dimension reduction technique based on the concepts of statistical linearization and of stochastic averaging for determining the non-stationary system response amplitude probability density functions (PDFs); thus, computationally intensive Monte Carlo simulations are circumvented. Note that the approach can handle readily stochastic excitations of arbitrary non-separable evolutionary power spectrum (EPS) forms that exhibit strong variability in both the intensity and the frequency content. Further, approximate closed-form expressions are derived for the non-stationary inter-story drift ratio amplitude PDFs corresponding to each and every DOF. In this regard, considering appropriately defined damage measures structural system related fragility curves are determined at a low computational cost as well. Finally, the structural system design optimization problem is formulated as a multi-objective one to be solved by a Genetic Algorithm based approach. A building structure comprising the versatile Bouc-Wen (hysteretic) model serves as an example for demonstrating the efficiency of the methodology.
AB - A performance-based multi-objective design optimization framework for nonlinear/hysteretic multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic excitation is formulated. The core of the developed framework is an efficient approximate dimension reduction technique based on the concepts of statistical linearization and of stochastic averaging for determining the non-stationary system response amplitude probability density functions (PDFs); thus, computationally intensive Monte Carlo simulations are circumvented. Note that the approach can handle readily stochastic excitations of arbitrary non-separable evolutionary power spectrum (EPS) forms that exhibit strong variability in both the intensity and the frequency content. Further, approximate closed-form expressions are derived for the non-stationary inter-story drift ratio amplitude PDFs corresponding to each and every DOF. In this regard, considering appropriately defined damage measures structural system related fragility curves are determined at a low computational cost as well. Finally, the structural system design optimization problem is formulated as a multi-objective one to be solved by a Genetic Algorithm based approach. A building structure comprising the versatile Bouc-Wen (hysteretic) model serves as an example for demonstrating the efficiency of the methodology.
UR - http://www.scopus.com/inward/record.url?scp=84978699101&partnerID=8YFLogxK
U2 - 10.14288/1.0076149
DO - 10.14288/1.0076149
M3 - Paper
T2 - 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012
Y2 - 12 July 2015 through 15 July 2015
ER -