Abstract

For seismic resilience-based design (RBD), a selection of recorded time histories for dynamic structural analysis is usually required. In order to make individual structures and communities regain their target functions as promptly as possible, uncertainty of the structural response estimates is in great need of reduction. The ground motion (GM) selection based on a single target response spectrum, such as acceleration or displacement response spectrum, would bias structural response estimates leading significant uncertainty, even though response spectrum variance is taken into account. In addition, resilience of an individual structure is not governed by its own performance, but depends severely on the performance of other systems in the same community. Thus, evaluation of resilience of a community using records matching target spectrum at whole periods would be reasonable because the fundamental periods of systems in the community may be varied. This paper presents a GM selection approach based on a probabilistic framework to find an optimal set of records to match multiple target spectra, including acceleration and displacement response spectra. Two major steps are included in that framework. Generation of multiple sub-spectra from target displacement response spectrum for selecting sets of GMs was proposed as the first step. Likewise, the process as genetic algorithm (GA), evolvement of individuals previously generated, is the second step, rather than using crossover and mutation techniques. A novel technique improving the match between acceleration response spectra of samples and targets is proposed as the second evolvement step. It is proved computationally efficient for the proposed algorithm by comparing with two developed GM selection algorithms. Finally, the proposed algorithm is applied to select GM records according to seismic codes for analysis of four archetype reinforced concrete (RC) frames aiming to evaluate the influence of GM selection considering two design response spectra on structural responses. The implications of design response spectra especially the displacement response spectrum and GM selection algorithm are summarized.

Highlights

  • Dynamic response history analysis is commonly used in performance-based earthquake engineering (PBEE) to estimate the response of structure subjected to ground motion records representing potential earthquakes in future

  • The design spectra including displacement response spectrum can only be chosen as target spectra to identify the engineering demand parameters (EDP) under records selected in terms of the counterpart seismic intensity

  • A computationally effective ground motions (GM) selection algorithm was proposed to select a set of recorded time histories whose acceleration and displacement response spectra match the targets

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Summary

Introduction

Dynamic response history analysis is commonly used in performance-based earthquake engineering (PBEE) to estimate the response of structure subjected to ground motion records representing potential earthquakes in future. The design spectra including displacement response spectrum can only be chosen as target spectra to identify the engineering demand parameters (EDP) under records selected in terms of the counterpart seismic intensity It must increase the complexity of GM selection as both the means and variances of both response spectra are taken into account. It is a notable multi-objective optimization that the greedy algorithm is not easy to resolve since the number of GM records matching target response. The most compatible GM set, namely having the closest scaling factors for both spectra, was selected as the optimal solution

Review of Displacement Response Spectra from International Seismic Codes
Framework of the New GM Selection Algorithm
Ground Motion Database and Scaling Approach
Generation of Initial Population
Non-Dominated Sorting of Initial Populations
Evolving Based on the Distribution-Scaled Method
Selecting the Optimal Solution
Example of PMDS Method on GM Selection According to TDSI
GM Selection Based on PMDS
GM Selection Based on Baker’s Greedy Method
Numerical Model of Reinforcement Concrete Frames
Findings
Conclusions
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