Evaluation of the Coefficient Method for estimation of maximum roof displacement demand of existing buildings subjected to near-fault ground motions

Yaghmaei‐Sabegh, Saman; Neekmanesh, Shabnam; Ruiz‐García, Jorge

doi:10.1016/j.soildyn.2019.03.015

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…e constant-strength inelastic displacement ratios play an important role in the displacement coefficient method (DCM), which is a convenient method to estimate the lateral displacement demand of structures for the seismic rehabilitation of existing buildings and has been incorporated in the ASCE/SEI 41-17 standards [37]. e prediction equations of the inelastic displacement ratios for pulse-like ground motions proposed by previous studies have been adopted or examined to estimate the seismic demand of structures to pulse-like ground motions [38,39]. e constant-ductility inelastic displacement ratios are vital in the design of new structures for displacement-based design procedures, such as the so-called displacement-based design via inelastic displacement ratio approach [40].…”

Section: Equations For the Mean Inelastic Displacement Ratiosmentioning

confidence: 99%

Effects of the Predominant Pulse on the Inelastic Displacement Ratios of Pulse‐Like Ground Motions Based on Wavelet Analysis

Zhao

Zhu

et al. 2021

Advances in Civil Engineering

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Having a predominant pulse is the main feature for pulse-like ground motions differing from others. To investigate the influence of the predominant pulse on the inelastic displacement ratios of pulse-like ground motions, the wavelet analysis method is used to extract the predominant pulse. The results indicate that the inelastic displacement ratios of the pulse-removed parts obtained by subtracting the extracted pulse from the original pulse-like ground motions are close to the results of non-pulse-like ground motions. The ratio of the energy of the extracted pulse to the energy of the original ground motion is used to represent the pulse intensity. The results indicate that the pulse period determines the locations in which the inelastic displacement ratios would have noticeable increments, and the pulse intensity determines the degree of the increments. Besides, the effects of five commonly used parameters (PGV, PGD, PGV/PGA, Arias intensity Ia, and soil condition) on the inelastic displacement ratios of pulse-like ground motions and their relations to the pulse period and the pulse intensity are studied. Finally, a new model, in which the influence of pulse intensity is considered, to predict the inelastic displacement ratios of pulse-like ground motions is proposed.

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Section: Equations For the Mean Inelastic Displacement Ratiosmentioning

confidence: 99%

Effects of the Predominant Pulse on the Inelastic Displacement Ratios of Pulse‐Like Ground Motions Based on Wavelet Analysis

Zhao

Zhu

et al. 2021

Advances in Civil Engineering

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“…The response spectrum will also be transformed into ADRS form to estimate the performance point using one of three algorithms. Conversely, the Displacement coefficient method (DCM) [11] uses modification coefficients (C0, C1, C2, and C3) to estimate the target displacement. In FEMA 440 [12], both methods were changed into equivalent linearization and modified coefficient methods.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for seismic demand prediction of a single degree of freedom

BENBOKHARI,

BENAZOUZ,

MEBARKI

2023

ICAENS

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This paper proposes using an artificial neural network (ANN) to estimate and predict the seismic demand of Single Degree of Freedom (SDOF) systems. Our methodology entails the production of a comprehensive dataset containing SDOF and earthquake characteristics. Nonlinear Time History Analysis (NL-THA) is performed on a randomly generated SDOF system using thirty-one artificial ground motions (GMs) matched to the EuroCode-8 (EC8) response spectrum to train the ANN model. To assess the performance of the ANN model, we compare the Incremental Dynamic Analysis (IDA) curves, the median IDA curve, and the 3D fragility surface in a case study. This analysis assists in determining the precision and dependability of the predicted maximum displacement of the SDOF system. The results showed a remarkable reduction in processing time without losing prediction accuracy. . It was concludedthat the ANN-based method can be used as an alternative for the current method for estimating the performance points and the fragility assessment of buildings.

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“…The Federal Emergency Management Agency (FEMA) and the Applied Technology Council (ATC) (16) propose many procedures that allow us to estimate the performance point and the target displacement, which is the maximum displacement of a building. The Displacement coefficient method proposed by FEMA 356 (17) uses the four coefficients C0, C1, C2, and C3 to calculate the target displacement of a building. These coefficients are calculated and calibrated using empirical data.…”

Section: Introductionmentioning

confidence: 99%

Dynamic response estimation of an equivalent single degree of freedom system using artificial neural network and nonlinear static procedure

Abdellatif,

Benazouz,

Ahmed

2023

Res. Eng. Struct. Mat.

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This paper introduces an innovative methodology for predicting the maximum dynamic response of structures using capacity curves and artificial neural networks (ANNs). This novel approach offers a quick and accurate procedure for estimating target displacements, obviating the need for intricate supplementary computations. The method generates a comprehensive dataset encompassing the bilinear representation of a single-degree-of-freedom (SDOF) characteristic, with ground motion parameters as inputs and maximum inelastic displacement as the corresponding output. This dataset is used to train an ANN model, with meticulous calibration of hyperparameters to ensure optimal model performance and predictive precision. The findings of this study demonstrate that the ANN model showed operational efficacy in approximating dynamic displacements. It is notably revealed that the size of the dataset significantly influences the ANN's performance and predictive accuracy. Through comparative analysis with established methodologies such as the displacement coefficient method and the modified coefficient method adopted by the Federal Emergency Management Agency (FEMA), the ANN model emerges as a fast tool for precisely predicting the dynamic response of single-degree-of-freedom systems, particularly those characterized by vibration periods exceeding 0.5 seconds. Consequently, this research culminates in the assertion that the ANN, owing to its inherent simplicity and impressive precision, is an alternative tool for estimating target displacements.

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Evaluation of the Coefficient Method for estimation of maximum roof displacement demand of existing buildings subjected to near-fault ground motions

Cited by 10 publications

References 6 publications

Effects of the Predominant Pulse on the Inelastic Displacement Ratios of Pulse‐Like Ground Motions Based on Wavelet Analysis

Effects of the Predominant Pulse on the Inelastic Displacement Ratios of Pulse‐Like Ground Motions Based on Wavelet Analysis

Artificial Neural Networks for seismic demand prediction of a single degree of freedom

Dynamic response estimation of an equivalent single degree of freedom system using artificial neural network and nonlinear static procedure

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