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Selection of earthquake ground motions for multiple objectives using genetic algorithms

Mergos, P.E. ORCID: 0000-0003-3817-9520 and Sextos, A. (2019). Selection of earthquake ground motions for multiple objectives using genetic algorithms. Engineering Structures, 187, pp. 414-427. doi: 10.1016/j.engstruct.2019.02.067

Abstract

Existing earthquake ground motion (GM) selection methods for the seismic assessment of structural systems focus on spectral compatibility in terms of either only central values or both central values and variability. In this way, important selection criteria related to the seismology of the region, local soil conditions, strong GM intensity and duration as well as the magnitude of scale factors are considered only indirectly by setting them as constraints in the pre-processing phase in the form of permissible ranges. In this study, a novel framework for the optimum selection of earthquake GMs is presented, where the aforementioned criteria are treated explicitly as selection objectives. The framework is based on the principles of multi-objective optimization that is addressed with the aid of the Weighted Sum Method, which supports decision making both in the pre-processing and post-processing phase of the GM selection procedure. The solution of the derived equivalent single-objective optimization problem is performed by the application of a mixed-integer Genetic Algorithm and the effects of its parameters on the efficiency of the selection procedure are investigated. Application of the proposed framework shows that it is able to track GM sets that not only provide excellent spectral matching but they are also able to simultaneously consider more explicitly a set of additional criteria.

Publication Type: Article
Additional Information: © Elsevier 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: seismic assessment; ground motion selection and scaling; responsehistory analysis; multi-objective optimization; genetic algorithms
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Civil Engineering
URI: http://openaccess.city.ac.uk/id/eprint/21732
[img] Text - Accepted Version
This document is not freely accessible until 7 March 2020 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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