Development of a Data-Mining Technique for Regional-Scale Evaluation of Building Seismic Vulnerability

Zhang, Zhenyu; Hsu, Ting-Yu; Wei, Hsi Hsien; Chen, Jieh Haur

doi:10.3390/app9071502

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…At a regional scale, data‐driven vulnerability assessment of buildings has been studied in an offline approach (Y. Liu, Li, Wei, Li, & Fu, 2019; Riedel et al., 2015; Zhang, Hsu, Wei, & Chen, 2019). Through the recent improvements in vision‐based learning methods, remote sensing is also widely studied for regional damage assessment (Xiong, Li, & Lu, 2020).…”

Section: Introductionmentioning

confidence: 99%

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

Sheibani

2020

Computer aided Civil Eng

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Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.

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Section: Introductionmentioning

confidence: 99%

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

Sheibani

2020

Computer aided Civil Eng

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“…SVM performs adequately even for the provided information in the context of unstructured and semi-structured data, such as tests of images merged with trees. Kernel trick is one of SVM's strengths that integrates all the knowledge required for the learning algorithm to identify a core object in the transformed field [35]. The implementation of SVM with initially supervised learning data-sets is seen in this analysis.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

et al. 2020

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Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

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“…To begin with, there are many variables and uncertainties that center around the evaluation process (number of victims, location and amount of damage, characteristics of the seismological phenomenon, presence of attenuating or amplifying local factors, influence of urban and building design, etc.) [1]. These variables tend to belong to different fields of knowledge, such as structural dynamics [2], earthquake engineering [3], geotechnics and geology [4], seismology [5], or even the urban configuration of cities [6].…”

Section: Introductionmentioning

confidence: 99%

The Spatial Perspective in Post-Earthquake Evaluation to Improve Mitigation Strategies: Geostatistical Analysis of the Seismic Damage Applied to a Real Case Study

2019

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Featured Application: Implementation of an innovative approach in the field of seismic damage analysis in cities to hierarchize the importance of the different phenomena detected. Possible applications to develop improved mitigation strategies in urban areas.Abstract: The analysis of damage in cities after an earthquake to implement mitigation strategies of seismic risk is a complex job that is usually full of uncertainties. Numerous variables affect the final result of the observable damage in a set of buildings in an urban area. The use of methodologies capable of providing global explanations beyond the traditional unidisciplinary approach of disciplines, such as structural analysis, earthquake engineering, geotechnics, or seismology, can be very useful for improving the behavior of our cities against earthquakes. This article presents geostatistical post-earthquake analysis, an innovative approach in this field of research based on GIS spatial statistical tools to evaluate the importance of the different variables after an earthquake that may have caused damage in a city. This new framework will be applied to analyze, from a geostatistical perspective, the damage levels observed in the city of Lorca (Spain) after the earthquake of 2011; a case study where various studies have proposed different measures to mitigate the impact of future earthquakes as a consequence of focusing on different phenomena as the main variable for the damage produced. A bivariate GIS assessment will allow spatial correlation of the problems detected from a statistical point of view (inadequate design of buildings, age of the real estate stock, inefficient urban planning configurations, geological risk, etc.) and the different levels of damage that the technicians who participated in the post-earthquake phase evaluated in the city. The results obtained will allow one to hierarchize the importance of the different detected phenomena to prepare the city better against future earthquakes and to elaborate an improved seismic mitigation strategy.

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Development of a Data-Mining Technique for Regional-Scale Evaluation of Building Seismic Vulnerability

Cited by 14 publications

References 30 publications

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

The Spatial Perspective in Post-Earthquake Evaluation to Improve Mitigation Strategies: Geostatistical Analysis of the Seismic Damage Applied to a Real Case Study

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