Review on Rapid Seismic Vulnerability Assessment for Bulk of Buildings

Nanda, Radhikesh Prasad; Majhi, Deepti Ranjan

doi:10.1007/s40030-013-0048-5

Cited by 18 publications

(10 citation statements)

References 8 publications

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“…The newest (third) edition of FEMA 154 is known as FEMA P-154 and was revised in 2015 [13]. The revised changes information was given in a scientific study by Nanda et al [23]. In FEMA P-154, the person performing the RVS procedure should fill out a data collection form by conducting a visual inspection of the building from the outside and, if possible, inside during the sidewalk survey.…”

Section: Usa Methods (Fema P-154)mentioning

confidence: 99%

“…There are many other RVS methods developed by using linear regression [15,16], multi-criteria decision making [17], machine learning techniques [18], fuzzy logic [19,20], or some other methods concerning their consideration and experiences on region or country [21][22][23], but the focus in this paper is on the most common RVS methodologies that have been provided by national technical codes and mentioned in the following [8,23].…”

Section: Rapid Visual Screeningmentioning

confidence: 99%

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Earthquake Safety Assessment of Buildings through Rapid Visual Screening

et al. 2020

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Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.

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Section: Usa Methods (Fema P-154)mentioning

confidence: 99%

Section: Rapid Visual Screeningmentioning

confidence: 99%

Earthquake Safety Assessment of Buildings through Rapid Visual Screening

et al. 2020

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“…FEMA proposed the first RVS manual in 1988, which was revised in 2002, by incorporating technological advancements of the time. The latest revision published in 2015 was named as FEMA P-154 [20]. The Greek Method (OASP) developed in 2000, classifies buildings into 18 structural types based on primary lateral load resisting system and structural material.…”

Section: Rapid Visual Screeningmentioning

confidence: 99%

“…The FEMA has published a standard set of guidelines for seismic risk assessment. This approach involves assigning a base score to the building based on the primary lateral force resisting system [20]. The performance modifiers shall take into account the effects of the plan and vertical irregularities (7 vertical and 5 plan irregularities, each divided into severe and moderate irregularities), post-benchmark attribute (when a structure is constructed after enforcement of seismic codes) or pre-code penalty (when the structure is constructed before the adoption of seismic codes) and soil type (only Type A, B, and E) as per data collection form.…”

Section: Rvs In the United States (Fema P-154)mentioning

confidence: 99%

A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

et al. 2020

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Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.

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“…In addition to national and local RVS methods, there are many other RVS methods developed by using linear regression [16,17], Multi-criteria decision making [3], Artificial Neural Networks (ANNs) [18][19][20][21], Fuzzy Logic (see Table 1), and some other methods concerning their consideration and experiences on a region or country scale [22]. These methods can be categorized into two groups: (a) methods based on statistical and machine learning approaches, like linear regression and ANN, and (b) methods based on expert systems such as fuzzy-based methods.…”

Section: Review Of Rapid Visual Screening Proceduresmentioning

confidence: 99%

Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model

Harirchian

Lahmer

2020

Applied Sciences

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Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey.

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Review on Rapid Seismic Vulnerability Assessment for Bulk of Buildings

Cited by 18 publications

References 8 publications

Earthquake Safety Assessment of Buildings through Rapid Visual Screening

Earthquake Safety Assessment of Buildings through Rapid Visual Screening

A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model

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