The influence of seismic intensity parameters on structural damage of RC buildings using principal components analysis

Massumi, Ali; Gholami, Fatemeh

doi:10.1016/j.apm.2015.09.043

Cited by 32 publications

(18 citation statements)

References 13 publications

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“…A general evaluation of relationships between various engineering demand parameters (EDPs) and cumulative-, spectrum-, and amplitude-based GMIMs including AI and CAV is summarized in Cosenza and Manfredi (2000), Riddell (2007), Kadaş and Yakut (2013), Ye et al (2013), Mollaioli et al (2013), Ebrahimian et al (2015), and Kostinakis et al (2018). In addition, several investigators have found that AI, CAV, or both sometimes in combination with other GMIMs are optimally correlated with EDPs proposed for buildings and bridges (e.g., Padget et al 2008, Fontara et al 2011, Katona 2012, Elenas 2013, Mollaioli et al 2013, Katona and Tóth 2013a, 2013b, Ebrahimian et al 2015, Hancilar and Çakti 2015, Perrault and Guéguen 2015, Tarbali and Bradley 2015, Massumi and Gholami 2016, Kiani and Pezeshk 2017, Muin and Mosalam 2017, Fiore et al 2018, Jahangiri et al 2018, Kiani et al 2018, Liang et al 2018, Mashayekhi et al 2018, Wang et al 2018). Other investigators have developed relationships correlating these GMIMs with amplitude- and spectrum-based GMIMs (e.g., Wang and Du 2012, Bradley 2012, Du and Wang 2013a, Liu et al 2016, Xu et al 2016) and ground motion duration measures (Bradley 2011).…”

Section: Introductionmentioning

confidence: 99%

Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

Campbell¹,

Bozorgnia

2019

Earthquake Spectra

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We updated our Next Generation Attenuation (NGA)-West1 ground motion models (GMMs) for the horizontal components of Arias intensity (AI) and cumulative absolute velocity (CAV) using the functional form and NGA-West2 database we used to develop GMMs for peak-amplitude and peak-spectral ground motion intensity measures (GMIMs). Our results show that CAV has the best goodness-of-fit statistics of all the GMIMs we have evaluated up to this time. Its relatively small between- and within-event standard deviations confirm its superior predictability. On the other hand, AI has the highest standard deviation of any GMIM we have studied thus far, which is approximately double that of CAV. Although either CAV or AI or a combination of both have been shown to meet various performance metrics proposed in the context of performance-based earthquake engineering (PBEE), CAV's high level of predictability makes it superior to AI for use in engineering applications, such as PBEE, that involve probabilistic inference.

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Section: Introductionmentioning

confidence: 99%

Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

Campbell¹,

Bozorgnia

2019

Earthquake Spectra

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“…Figure 5 shows the corresponding accelerogram signalẍ g (t). In the next section the Arias Intensity (i A ) is used to quantify the earthquake acceleration power [3]. The Arias Intensity is defined as:…”

Section: Algorithm 1 Stochastic Accelerogram Generationmentioning

confidence: 99%

On the Effect of Nonlinear Energy Sink Damping in Seismic Vibration Attenuation

Motato

Guerrero

2021

Preprint

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Nonlinear Energy Sinks (NESs) have been proposed for passively reducing the amplitude of vibrations in different types of structures. The main advantage of NES over traditional Tuned Mass Dampers (TMDs) lies in its capability to redistribute the vibrating energy inside a primary structure, what effectively reduces the amplitude of the structure oscillations over a wide range of frequencies. However, the performance of an NES can be substantially affected even by small variations on input energy as in the case of buildings under seismic ground excitation. In this work it is shown that the NES energy sensibility can be significantly reduced by properly selecting the NES damping coefficient. A three stories shear building model subject to seismic ground excitation is used to numerically study the effect that NES damping has on its vibration reduction performance.

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“…In Refs. [8,9] principal component analysis (PCA) is used to select feature. Other feature selection methods for bridge structural diagnosis include local linear embedding (LLE) [10,11], independent component analysis (ICA) [12,13], isometric mapping algorithm [14], and .…”

Section: Introductionmentioning

confidence: 99%

A Novel Bridge Damage Diagnosis Algorithm Based on Deep Learning with Gray Relational Analysis for Intelligent Bridge Monitoring System

Xiao

Wang

Dong

et al. 2021

IEEJ Transactions Elec Engng

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In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto-encoder-based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high-level features from raw signals via a multi-layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learningbased algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures.

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The influence of seismic intensity parameters on structural damage of RC buildings using principal components analysis

Abstract: Please cite this article as: Ali Massumi , Fatemeh Gholami , The influence of seismic intensity parameters on structural damage of RC buildings using principal components analysis, Applied Mathematical Modelling (2015),

Cited by 32 publications

References 13 publications

Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

On the Effect of Nonlinear Energy Sink Damping in Seismic Vibration Attenuation

A Novel Bridge Damage Diagnosis Algorithm Based on Deep Learning with Gray Relational Analysis for Intelligent Bridge Monitoring System

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