Seismic damage evaluation of unanchored nonstructural components under combined effects of horizontal and vertical near‐fault ground motions

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Estimating seismic demands and the associated damage to nonstructural components (NSCs) is a critical step in the performance‐based earthquake engineering (PBEE) framework. To facilitate this type of assessment, NSCs are classified as being acceleration, velocity, or displacement sensitive. When two‐dimensional structural models and response history analyses are used in the PBEE framework, heuristics are used to combine the orthogonal responses. Although there has been significant research to develop and evaluate combination rules for structural component responses, comparatively much less attention has been placed on NSC demands. FEMA P‐58 does provide a combination rule specifically for NSC seismic demands; however, the basis and accuracy of these provisions have never been examined. In this paper, the effectiveness of three popular combination rules used to estimate peak floor acceleration (PFA) and peak floor velocity (PFV) is investigated. Incremental dynamics analyses under unidirectional and bidirectional record‐sets are performed on six special concentrically braced frame buildings with different heights and torsional irregularity. The associated floor responses are used to examine the efficacy of the combination rules. Subsequently, a probabilistic approach is proposed to enhance the accuracy of combination rules. The performance of the enhanced combination rules is compared to that of a surrogate model that estimates PFA and PFV under orthogonal seismic effects. The results show that the performance of existing combination rules is slightly affected by building height and plan irregularity. Also, the combination rule suggested by FEMA P58 is found to overestimate the PFA and PFV demands by 10–15% compared to the demands derived from bidirectional response history analysis.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Probabilistic assessment of the rules used to combine peak floor responses of special concentrically braced frames under orthogonal seismic effects

Wang

Huang

Burton

et al. 2023

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Automated parameterization of velocity pulses in near‐fault ground motions

Chang,

Wu,

Goda

2023

Proper parameterization of near‐fault ground motions is of critical importance in earthquake engineering, and this process is traditionally performed by directly fitting an analytical pulse model to the original motion. Yet such a process is usually limited by the trial‐and‐error procedure, which is strongly dependent on the initial guesses and may converge to local rather than global minimums. In this study, we propose a progressive (step‐by‐step) iterative approach that can achieve a fully automated parameterization of the velocity pulse contained in near‐fault motions. Assuming that a velocity pulse can be characterized by a pulse model with four key parameters, the approach is conducted by iteratively matching the pulse model to the smoothed ground motion, and the parameterized pulse is analytically derived by best fitting to the smoothed motion not only in the time domain but also in the spectral domain. Specifically, the velocity time history of interest is initially smoothed by a moving average filter so that the low‐frequency content can be filtered out of the original motion, from which the pulse amplitude as well as its epoch is accordingly determined. The coherent velocity pulse is then progressively extracted by performing the nonlinear least‐square‐fitting of the pulse model to the filtered low‐frequency content, during which the remaining parameters, that is, the pulse period, the number of cycles and the phase of the pulse, are estimated successively. Finally, the above procedure is applied repeatedly to the original ground motion by changing an empirical factor controlling the extent of smoothing of the motion so that convergence to local minimums that frequently occurs in the trial‐and‐error procedure could be largely avoided, and best match of the spectrum of the extracted pulse to that of the original motion can be acquired. Fitting quality of the velocity pulses is examined by comparing with existing methods. Prospective applications of the proposed procedure include the stochastic simulations of near‐fault ground motions and parametric investigations of the influence of velocity pulses on various engineered structures.

Random‐positioned‐sampling effect on probabilistic seismic demand modeling of modularized suspended buildings with free‐standing objects and architectural function regioning

Ye,

Liu

2024

The definition of the targeted engineering demand parameters (EDPs) is important to probabilistic seismic demand modeling (PSDM), which produces probability density functions of EDPs conditioned on seismic intensity measure (IM). The targeted EDPs are usually defined at the group level to account for multiple components/units. Thus, they are affected by the considered range of units, i.e., the sample positions. For instance, the maximum peak floor acceleration (PFA) within the whole building differs from the maximum among only the important positions related to seismic loss. Additional uncertainties are induced in the PSDM of PFA if the sample positions vary when architectural function and non‐structural elements change. In this study, the aforementioned influence is termed random‐positioned‐sampling (RPS) effect, and it is investigated by targeting a modularized suspended building, which features the tuning mechanism, multiple major modes, uneven response envelopes, and notable non‐structural‐object‐structure interactions (NSOSI). Results show that the RPS effect lowers the maximum‐based group‐level EDP and increases the dispersion within the EDP sample sets, indicating that conventional PSDMs without considering the RPS effect are biased. The significance of the influence is positively correlated to the position‐wise coefficient of variation of EDP but negatively correlated to the density of sample positions. The combined influence of the NSOSI and the RPS effect is two‐sided for PSDM. The NSOSI amplifies the RPS effect via enlarging position‐wise dispersion of EDP, whereas, the RPS effect waives part of the detrimental scattered contributions from NSOSI. Overall, the IM performance is handicapped, even with IM optimization. However, it can be compensated if architectural function region information is acquired beforehand since the sample positions are restrained.