Seismic response of nonstructural components considering the near-fault pulse-like ground motions

Zhai, Changhai; Zheng, Zhenlong; Li, Shuang; Pan, Xiaolan; Xie, Lili

doi:10.12989/eas.2016.10.5.1213

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…Wei Jiang et al [17] established floor response spectra to assess the seismic demands on nuclear facilities, finding that FRS generated from time history analysis exhibited significant variations, especially in specific tuning scenarios. The investigation of floor response spectra for multi-story structures has been a subject of study [18][19][20][21][22]. Furthermore, the impact of stiffness irregularities on the FRS was examined [23], with the research revealing heightened acceleration amplification at the soft story level.…”

Section: Introductionmentioning

confidence: 99%

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Pesaralanka,

Burugapalli,

Challagulla

2024

Res. Eng. Struct. Mat.

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Historical earthquakes highlighted structural collapses and non-structural component (NSC) failures. This study aims to analyze NSC behavior during near and far-field earthquakes in a three-story building by studying elastic and inelastic acceleration responses. The investigation delves into floor response spectra and analyzes how NSCs affect floor acceleration responses, aiming to understand the performance of these components. The findings from the analysis indicated that the Floor Response Spectra (FRS) consistently align peaks with elastic and inelastic modal periods. Inelastic FRS notably show reduced floor spectral accelerations compared to elastic FRS. Near-field earthquakes induce 25-30% higher floor acceleration demands in NSCs compared to far-field earthquakes. Peak Component Acceleration (PCA) values differ significantly between near and far field earthquakes, with near-field ones exhibiting notably higher values across all floors. Higher damping ratios in NSCs lead to decreased peaks in the Component Dynamic Amplification Factor (CDAF) spectrum. The inelastic model notably reduces peak values of CDAF by approximately 49.53% to 69.3% for near-field and 51.81% to 64.47% for far-field earthquakes compared to the elastic model. In summary, the examination of peak floor responses against the formulation based on building codes highlights variations, with instances where the formulation either underestimates or overestimates the peak response demands.

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Section: Introductionmentioning

confidence: 99%

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Pesaralanka,

Burugapalli,

Challagulla

2024

Res. Eng. Struct. Mat.

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“…Their findings indicated that floor spectra derived from dynamic analysis exhibited notable fluctuations, particularly in tuned conditions. Extensive research has been conducted on the FRSs of multi-story buildings [13][14][15][16][17]. Reinoso and Miranda [18] found that earthquake-induced floor acceleration demands in high-rise buildings can significantly deviate from current US seismic guidelines.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

Vyshnavi,

Challagulla,

Adamu

et al. 2023

Applied Sciences

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Soft stories in buildings are well-known to present structural vulnerabilities during seismic events, and the failure of non-structural components (NSCs) has been evident in past earthquakes, along with structural damage. This study seeks to investigate how the presence of a soft story in a building affects the criteria for elastic floor acceleration. The soft story is assumed to be at the top, middle, and bottom levels of the structure. To comprehend the behavior of NSCs, the researchers analyze the floor response spectra (FRSs) and component acceleration amplification. Remarkably, the results reveal that the position of the soft story strongly influences the floor response spectra, with structures featuring a middle soft story showing the most significant amplification of component acceleration. In constructing the FRSs, the component dynamic amplification factors (CDAFs) play a vital role as they accurately illustrate how NSCs amplify floor vibrations. Consequently, the study delves into exploring machine learning (ML) models like artificial neural networks (ANNs) and random forest (RF) to map the intricate relationship between CDAFs, the dynamic characteristics of the building, and the behavior of NSCs. Upon comparison of the two models, the random forest model emerges as the superior method in predicting the CDAFs.

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“…They found that the FRS from analysis showed significant fluctuations, especially in tuning circumstances. Investigations have been done on the floor response spectrum of complex structures [20][21][22][23][24]. The most recent research [25] investigated how a stiffness irregularity affected the FRS and found that the floor response's amplitude is larger at the soft story position.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors

Bhavani,

Challagulla,

Noroozinejad Farsangi

et al. 2023

IJE

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The seismic performance of non-structural components (NSCs) has been the focus of intensive study during the last few decades. Modern building codes define design forces on components using too simple relationships. The component accelerates faster than the floor acceleration to which it is connected. Therefore, component dynamic amplification factors (CDAFs) are calculated in this work to quantify the amplification in the acceleration of NSCs for the various damping ratios and tuning ratios of the NSC, and the primary structural periods. From the analysis results, it was observed that CDAF peaks are either underestimated or overestimated by the code-based formulae. A prediction model to ascertain the CDAFs was also developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation (𝑅) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.

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Seismic response of nonstructural components considering the near-fault pulse-like ground motions

Cited by 11 publications

References 28 publications

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors

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