Seismic vulnerability assessment using regional empirical data

Yakut, Ahmet; Özcebe, Güney; Yücemen, M. Semih

doi:10.1002/eqe.572

Cited by 43 publications

(21 citation statements)

References 12 publications

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“…Noncritical infrastructure Probablisitc Empirical/ Statistical Yakut et al (2006) Important building/facility Magnitude Frequency Relation Yucemen et al (2004) Infrastr. Network analysis Kiremidjian et al (2007) Local and regional studies Damage Index Analytical Park and Ang (1985) Detailed Structural Gulkan and Sozen(1999) Hazard distribution Bozorgnia & Bertero(2003) functions HAZAUS (2001) III Heuristic…”

Section: Classification Of Seismic Risk Modelsmentioning

confidence: 99%

“…Yucemen et al (2004) proposed a simplified damage index to estimate the seismic vulnerability of low-rise to mid-rise reinforced concrete buildings. Yakut et al (2006) developed a scoring system for estimating the damage within low rise buildings using different structural and seismic modifiers. Park and Ang (1985) developed an analytical damage index for estimating the vulnerability of RC buildings.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

“…Currently, the majority of existing models has primarily focused on the structural system performance, building capacity, layout and certain response parameters. Detailed risk analysis relying on a comprehensive data collection that are generally employed for the assessment of individual buildings, as they require sophisticated modelling, thereby aim to determine whether the building needs rehabilitation (Yakut et al 2006). Although detailed analysis provides high-precision results, it is restricted to individual case studies and thus cannot be used for regional studies in which a large number of buildings is involved.…”

Section: Eguchi and Seligson (2008) Note About Evaluation Pitfalls Thmentioning

confidence: 99%

See 2 more Smart Citations

Seismic risk management: A system-based perspective

2014

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The deployment of seismic risk management is fraught with issues of complexity, ambiguity and uncertainty which pose critical challenges in assessing, modelling and management. The complexity of earthquake impacts and the uncertain nature of information necessitate establishing a risk management system to address the risk of many the effects of seismic events in a reliable and realistic way. This study was launched to review and criticize appropriate risk assessment methods for different seismic applications focusing on general characteristics from a systems perspective. The outcome of this study demonstrates the importance of a system perspective, providing a deeper insight into the background characteristics and the potential challenges involved within seismic risk management. This allows users to compare various systems and to choose the most appropriate approach according to the scope, size, accuracy and complexity of the application.

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Section: Classification Of Seismic Risk Modelsmentioning

confidence: 99%

Section: Probabilistic Modelsmentioning

confidence: 99%

Section: Eguchi and Seligson (2008) Note About Evaluation Pitfalls Thmentioning

confidence: 99%

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Seismic risk management: A system-based perspective

2014

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“…Three structural parameters were found to discriminate the data set the most: the number of storeys, a ratio of the ground storey height to the first storey height and a ratio of the column area to the floor area. Yakut et al [28] extended this approach and considered also local soil characteristics.…”

Section: Introductionmentioning

confidence: 98%

Prediction of seismic-induced structural damage using artificial neural networks

Lautour

Omenzetter

2009

Engineering Structures

156

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a b s t r a c tContemporary methods for estimating the extent of seismic-induced damage to structures include the use of nonlinear finite element method (FEM) and seismic vulnerability curves. FEM is applicable when a small number of predetermined structures is to be assessed, but becomes inefficient for larger stocks. Seismic vulnerability curves enable damage estimation for classes of similar structures characterised by a small number of parameters, and typically use only one parameter to describe ground motion. Hence, they are unable to extend damage prognosis to wider classes of structures, e.g. buildings with a different number of storeys and/or bays, or capture the full complexity of the relationship between damage and seismic excitation parameters. Motivated by these shortcomings, this study presents a general method for predicting seismic-induced damage using Artificial Neural Networks (ANNs). The approach was to describe both the structure and ground motion using a large number of structural and ground motion properties. The class of structures analysed were 2D reinforced concrete (RC) frames that varied in topology, stiffness, strength and damping, and were subjected to a suite of ground motions. Dynamic structural responses were simulated using nonlinear FEM analysis and damage indices describing the extent of damage calculated. Using the results of the numerical simulations, a mapping between the structural and ground motion properties and the damage indices was than established using an ANN. The performance of the ANN was assessed using several examples and the ANN was found to be capable of successfully predicting damage.

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“…Several studies have proposed different vulnerability assessment methodologies using a variety of vulnerability parameters (Yin et al, 1990;Singhal and Kiremidjian, 1996;Hwang et al, 1997;Kircher et al, 1997;Shinozuka et al 2000;Porter et al, 2001;Yakut et al, 2006). Some representative methodologies are as follows.…”

Section: Introductionmentioning

confidence: 99%

Performance-based methodology for assessing seismic vulnerability and capacity of buildings

Lin

Xie

Gong

et al. 2010

Earthq. Eng. Eng. Vib.

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This paper presents a performance-based methodology for the assessment of seismic vulnerability and capacity of buildings. The vulnerability assessment methodology is based on the HAZUS methodology and the improved capacitydemand-diagram method. The spectral displacement (S d ) of performance points on a capacity curve is used to estimate the damage level of a building. The relationship between S d and peak ground acceleration (PGA) is established, and then a new vulnerability function is expressed in terms of PGA. Furthermore, the expected value of the seismic capacity index (SC ev ) is provided to estimate the seismic capacity of buildings based on the probability distribution of damage levels and the corresponding seismic capacity index. The results indicate that the proposed vulnerability methodology is able to assess seismic damage of a large number of building stock directly and quickly following an earthquake. The SC ev provides an effective index to measure the seismic capacity of buildings and illustrate the relationship between the seismic capacity of buildings and seismic action. The estimated result is compared with damage surveys of the cities of Dujiangyan and Jiangyou in the M8.0 Wenchuan earthquake, revealing that the methodology is acceptable for seismic risk assessment and decision making. The primary reasons for discrepancies between the estimated results and the damage surveys are discussed.

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Seismic vulnerability assessment using regional empirical data

Cited by 43 publications

References 12 publications

Seismic risk management: A system-based perspective

Seismic risk management: A system-based perspective

Prediction of seismic-induced structural damage using artificial neural networks

Performance-based methodology for assessing seismic vulnerability and capacity of buildings

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