Development in Fuzzy Logic-Based Rapid Visual Screening Method for Seismic Vulnerability Assessment of Buildings

Bektaş, Nurullah; Kegyes-Brassai, Orsolya

doi:10.3390/geosciences13010006

Cited by 18 publications

(13 citation statements)

References 42 publications

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“…Structures with plan irregularities are labeled Yes, The corresponding explanations involved in determining the parameters considered for a FIS-based RVS technique developed in this investigation are provided below, one by one. Furthermore, additional in-depth explanations of these parameters can be found in the authors' earlier study [88] in addition to the ones provided below.…”

Section: Determining Rvs Parametersmentioning

confidence: 91%

“…Accordingly, Figure 7 illustrates the percentages of each relevant input parameter in the considered data as a pie chart. Explanations of steps 1 through 6 for the development of the fuzzy logic-based RVS method are not included in this study; these are detailed in [88].…”

Section: Determining Rvs Parametersmentioning

confidence: 99%

“…The final scores obtained from the development fuzzy logic-based RVS method could be converted to linguistic variables based on the classification information provided in Table 2 to represent building safety levels linguistically. As a result, each parameter discussed under this title is extensively explained in another paper the authors have written [88].…”

Section: Determining Rvs Parametersmentioning

confidence: 99%

“…As a result, each parameter discussed under this title is extensively explained in another paper the authors have written [88].…”

Section: Fuzzy Logic Theory Fuzzy Inferencesmentioning

confidence: 99%

“…• Identify variables and corresponding parameters, Explanations of steps 1 through 6 for the development of the fuzzy logic-based RVS method are not included in this study; these are detailed in [88].…”

mentioning

confidence: 99%

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Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings

2022

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Many conventional rapid visual screening (RVS) methods for the seismic assessment of existing structures have been designed over the past three decades, tailored to site-specific building features. The objective of implementing RVS is to identify the buildings most susceptible to earthquake-induced damage. RVS methods are utilized to classify buildings according to their risk level to prioritize the buildings at high seismic risk. The conventional RVS methods are employed to determine the damage after an earthquake or to make safety assessments in order to predict the damage that may occur in a building before an impending earthquake. Due to the subjectivity of the screener based on visual examination, previous research has shown that these conventional methods can lead to vagueness and uncertainty. Additionally, because RVS methods were found to be conservative and to be partially accurate, as well as the fact that some expert opinion based developed RVS techniques do not have the capability of further enhancement, it was recommended that RVS methods be developed. Therefore, this paper discusses a fuzzy logic based RVS method development to produce an accurate building features responsive examination method for unreinforced masonry (URM) structures, as well as a way of revising existing RVS methods. In this context, RVS parameters are used in a fuzzy-inference system hierarchical computational pattern to develop the RVS method. The fuzzy inference system based RVS method was developed considering post-earthquake building screening data of 40 URM structures located in Albania following the earthquake in 2019 as a case study. In addition, FEMA P-154, a conventional RVS method, was employed to screen considered buildings to comparatively demonstrate the efficiency of the developed RVS method in this study. The findings of the study revealed that the proposed method with an accuracy of 67.5% strongly outperformed the conventional RVS method by 42.5%.

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Section: Determining Rvs Parametersmentioning

confidence: 91%

Section: Determining Rvs Parametersmentioning

confidence: 99%

Section: Determining Rvs Parametersmentioning

confidence: 99%

“…As a result, each parameter discussed under this title is extensively explained in another paper the authors have written [88].…”

Section: Fuzzy Logic Theory Fuzzy Inferencesmentioning

confidence: 99%

mentioning

confidence: 99%

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Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings

2022

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Complex Framework for Condition Assessment of Residential Buildings

Bukovics,

Lilik,

Kóczy

et al. 2023

Lecture Notes in Civil Engineering

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Developing a machine learning-based rapid visual screening method for seismic assessment of existing buildings on a case study data from the 2015 Gorkha, Nepal earthquake

Bektaş,

Kegyes-Brassai

2024

Bull Earthquake Eng

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Each existing building is required to be assessed before an impending severe earthquake utilizing Rapid Visual Screening (RVS) methods for its seismic safety since many buildings were constructed before seismic standards, without taking into account current regulations, and because they have a limited lifetime and safety based on how they were designed and maintained. Building damage brought on by earthquakes puts lives in danger and causes significant financial losses. Therefore, the fragility of each building needs to be determined and appropriate precautions need to be taken. RVS methods are used when assessing a large building stock since further in-depth vulnerability assessment methods are computationally expensive and costly to examine even one structure in a large building stock. RVS methods could be implemented in existing buildings in order to determine the damage potential that may occur during an impending earthquake and take necessary measures for decreasing the potential hazard. However, the reliability of conventional RVS methods is limited for accurately assessing large building stock. In this study, building inspection data acquired after the 2015 Gorkha, Nepal earthquake is used to train nine different machine learning algorithms (Decision Tree Classifier, Logistic Regression, Light Gradient Boosting Machine Classifier, eXtreme Gradient Boosting Classifier, Gradient Boosting Classifier, Random Forest Classifier, Support Vector Machines, K-Neighbors Classifier, and Cat Boost Classifier), which ultimately led to the development of a reliable RVS method. The post-earthquake building screening data was used to train, validate, and ultimately test the developed model. By incorporating advanced feature engineering techniques, highly sophisticated parameters were introduced into the developed RVS method. These parameters, including the distance to the earthquake source, fundamental structural period, and spectral acceleration, were integrated to enhance the assessment capabilities. This integration enabled the assessment of existing buildings in diverse seismically vulnerable areas. This study demonstrated a strong correlation between determining building damage states using the established RVS method and those observed after the earthquake. When comparing the developed method with the limited accuracy of conventional RVS methods reported in the literature, a test accuracy of 73% was achieved, surpassing conventional RVS methods by over 40% in accurately classifying building damage states. This emphasizes the importance of detailed data collection after an earthquake for the effective development of RVS methods.

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Development in Fuzzy Logic-Based Rapid Visual Screening Method for Seismic Vulnerability Assessment of Buildings

Cited by 18 publications

References 42 publications

Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings

Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings

Complex Framework for Condition Assessment of Residential Buildings

Developing a machine learning-based rapid visual screening method for seismic assessment of existing buildings on a case study data from the 2015 Gorkha, Nepal earthquake

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