Optimal intensity measure based on spectral acceleration for P-delta vulnerable deteriorating frame structures in the collapse limit state

Adam, Christoph; Kampenhuber, David; Ibarra, Luis

doi:10.1007/s10518-017-0129-3

Cited by 30 publications

(21 citation statements)

References 26 publications

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“…Adam et al [35] However, the abovementioned ground motion IMs only capture the effect of period elongation and do not reflect the effect of higher vibration modes for long-period structures. Some studies have pointed out that ground motion IMs considering the higher vibration mode are suitable for high-rise buildings.…”

Section: S a (T 1 )-Based Ground Motion Intensity Measuresmentioning

confidence: 99%

Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures

Zhang¹,

Shi²,

Sun³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Based on the multiple stripes analysis method, an investigation of the estimation of aleatory randomness by S a (T 1 )-based intensity measures (IMs) in the fragility analysis is carried out for two typical low-and mediumrise reinforced concrete (RC) frame structures with 4 and 8 stories, respectively. The sensitivity of the aleatory randomness estimated in fragility curves to various S a (T 1 )-based IMs is analyzed at three damage limit states, i.e., immediate occupancy, life safety, and collapse prevention. In addition, the effect of characterization methods of bidirectional ground motion intensity on the record-to-record variability is investigated. It is found that the damage limit state of the structure has an important influence on the applicability of the ground motion IM. The S a (T 1 )-based IMs, considering the effect of softened period, can maintain lower record-to-record variability in the three limit states, and the S a (T 1 )-based IMs, considering the effect of higher modes, do not show their advantage over S a (T 1 ). Furthermore, the optimal multiplier C and exponent α in the dual-parameter ground motion IM are proposed to obtain a lower record-to-record variability in the fragility analysis of different damage limit state. Finally, the improved dual-parameter ground motion IM is applied in the risk assessment of the 8-story frame structure.

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Section: S a (T 1 )-Based Ground Motion Intensity Measuresmentioning

confidence: 99%

Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures

Zhang¹,

Shi²,

Sun³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…We note that, applying the proposed neural network enhanced strategy, the incorporation of further intensity measures as input parameters can lead to an improvement regarding the accuracy of the response predictions. In particular, the geometric mean of the spectral acceleration [63][64][65][66] has shown to be a promising intensity measure and should be considered in future studies in this regard.…”

Section: Machine Learning Enhanced Evaluation Of Tail-end Probabilitiesmentioning

confidence: 99%

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Thaler

Stoffel

Markert

et al. 2021

Earthq Engng Struct Dyn

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Evaluating the response statistics of nonlinear structures constitutes a key issue in engineering design. Hereby, the Monte Carlo method has proven useful, although the computational cost turns out to be considerably high. In particular, around the design point of the system near structural failure, a reliable estimation of the statistics is unfeasible for complex high-dimensional systems. Thus, in this paper, we develop a machine-learning-enhanced Monte Carlo simulation strategy for nonlinear behaving engineering structures. A neural network learns the response behavior of the structure subjected to an initial nonstationary ground excitation subset, which is generated based on the spectral properties of a chosen ground acceleration record. Then using the superior computational efficiency of the neural network, it is possible to predict the response statistics of the full sample set, which is considerably larger than the initial training sample set. To K E Y W O R D Searthquake generation, elastoplastic structures, Kanai-Tajimi filter, machine learning, Monte Carlo method, neural networks This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Therefore, it is more appropriate to consider IMs calculated from the spectral response of SDoF systems, since they have been shown to be highly correlated with the MIDR (Miranda, 2000). In this respect, several researchers have proven that it is more efficient to use an IM based on the geometric mean of spectral acceleration values around the fundamental period, 𝑆𝑎 𝑎𝑣𝑔 , than only using the spectral acceleration value associated to this period, 𝑆𝑎(𝑇 1 ), (Bianchini et al, 2009;Bojórquez and Iervolino 2011;Kazantzi and Vamvatsikos 2015;Eads et al 2015;Adam et al 2017). Analogous to this concept, the arithmetic mean of spectral acceleration values around the fundamental period, AvSa, has been considered herein as IM to select and scale ground motion records.…”

Section: Gravity Loads and Mechanical Propertiesmentioning

confidence: 99%

“…Several researchers have proven that it is more efficient to use an IM based on the geometric mean of spectral acceleration values around the fundamental period, 𝑆𝑎 𝑎𝑣𝑔 , than 𝑆𝑎(𝑇 1 ), (Bianchini et al, 2009;Bojórquez and Iervolino 2011;Kazantzi and Vamvatsikos 2015;Eads et al 2015;Adam et al 2017). Note that 𝑆𝑎 𝑎𝑣𝑔 is analogous to AvSa since both IMs use averaged spectral acceleration ordinates over a period range around the fundamental period of the structure.…”

Section: 𝑆𝑎 𝑎𝑣𝑔 Vs Avsamentioning

confidence: 99%

New Insights Into the Relationship Between Seismic Intensity Measures and Nonlinear Structural Response

Alzate

Hurtado

Pujades

2021

Preprint

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This paper focuses on the probabilistic analysis of Intensity Measures (IMs) and Engineering Demand Parameters (EDPs) for earthquake excitations. Several statistical properties, which are desirable in IMs when they are used to predict EDPs, have been analyzed. The main sources of uncertainty involved in the calculation of the seismic risk have been considered in the analysis. Efficiency, sufficiency and steadfastness have been quantified for a set of IMs with respect to two EDPs: the maximum inter-storey drift ratio, MIDR, and the maximum floor acceleration, MFA. Steadfastness is a new statistical property proposed in this article. It is related to the ability of IMs to forecast EDPs for big building suites. This also means that efficiency does not significantly vary when different types of buildings are included in the statistical analyses. This property allows reducing the number of calculations when performing seismic risk estimations at urban level since, for instance, a large variety of fragility curves of specific buildings can be grouped together within an only one, but more generic, fragility function. The nonlinear dynamic response of probabilistic multi-degree-of-freedom buildings’ models, subjected to a large data set of ground motion records, have been considered to perform the statistical analysis. Specifically, reinforced concrete buildings whose number of stories vary from 3 to 13 stories have been analysed. 18 spectrum-, energy- and direct-accelerogram-based IMs have been considered harein. From the statistical properties of the generated clouds of IM-EDP points, efficiency and sufficiency properties have been quantified. For MIDR, results show that IMs based on spectral velocity are more efficient and steadfast than the ones based on spectral acceleration; spectral velocity averaged in a range of periods, AvSv, has shown to be the most efficient and steadfast IM. The opposite happens for MFA, that is, spectral acceleration-based-IMs are more efficient than the velocity-based ones. A comparison on the use of linear vs quadratic regression models, and their implications on the derivation of fragility functions, is presented as well. Concerning sufficiency, most of the 18 basic IMs analyzed herein do not have this property. However, multi-regression models have been employed to address this lack of sufficiency allowing to obtain a so-called ‘ideal’ IM.

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Optimal intensity measure based on spectral acceleration for P-delta vulnerable deteriorating frame structures in the collapse limit state

Cited by 30 publications

References 26 publications

Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures

Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

New Insights Into the Relationship Between Seismic Intensity Measures and Nonlinear Structural Response

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