Use of Artificial Neural Networks in the R/C Buildings’ Seismic Vulnerabilty Assessment: The Practical Point of View

Morfidis, Konstantinos; Kostinakis, Konstantinos

doi:10.7712/120119.7316.19299

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…Askan and Yucemen [33] suggested several probabilistic models, such as models based on reliability and "best estimate" matrices of the likelihood of damage for a different seismic zone by combining expert opinion and the damage statistics of past earthquakes. Morfidis and Kostinakis [34] have explicitly proved that ANN could be practically applied in RVS from an innovative perspective, carried forward by the pioneering application of the coupling of fuzzy logic and ANN's by Dristos [35]. In this research, untrained fuzzy logic procedures have shown exceptional performance.…”

Section: Background Of Studymentioning

confidence: 93%

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

et al. 2021

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A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.

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Section: Background Of Studymentioning

confidence: 93%

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

et al. 2021

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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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“…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 [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 Type-2 Fuzzy Logic Model

Harirchian¹,

Lahmer²

2020

Preprint

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Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritize 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. It 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 Visual 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 28 reinforced concrete buildings from the Bingöl earthquake in Turkey.

show abstract

Use of Artificial Neural Networks in the R/C Buildings’ Seismic Vulnerabilty Assessment: The Practical Point of View

Cited by 14 publications

References 15 publications

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

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

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

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