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

Abstract

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.

Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

“…This largely determines the use in this area of modern methods of machine learning and neural networks, which make it possible to effectively solve the problems of analysing the instrumental data and predicting the occurrence of various events. In [14] different supervised learning algorithms applied to Ecuador, Haiti, Nepal, and South Korean earthquakes data to classify damage grades to buildings. A ground motion prediction by ANN of MLP-architecture is considered in [15], performing training, validating, and testing on the Indian strong motion database including 659 records from 138 earthquakes.…”

“…The key aspect for the application of machine learning and deep learning methods is the preparation of a high-quality reference data set [14], labelled in accordance with the semantics of the study. This task is often solved manually, but when the amount of initial data is large, it requires the development of additional algorithms.…”

Assessment of Seismic Building Vulnerability Using Rapid Visual Screening Method through Web-Based Application for Malaysia

“…The following are the primary criteria that contributed to this screening score: the location of the building, the soil type, the duration or age of occupancy, the risk of falling, and others. Using the SPI index, researchers can group assessment into three stages: low detailing assessment (SPI less than 10) is deemed "low," medium detailing assessment (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and high detailing assessment (SPI greater than 20). SI is the structural index that was derived by multiplying five components, viz., (A) seismicity index; (B) effect of soil condition; (C) type of structure; (D) building irregularities; and (E) importance of the building.…”

“…The empirical method relies on the survey carried out before earthquakes, where the consistency of the method depends on the completeness of the data collection for past earthquakes. The empirical approach is used to formulate various vulnerability assessment models, such as empirical fragility functions, VI models that are based on empirical rating factors, and the Rapid Visual Screening (RVS) approach [13][14][15][16][17].…”

Vulnerability Management Models Using a Common Vulnerability Scoring System

“…Consequently, the tasks of detecting the most crucial issues and directing efforts to the most demanding fields are immensely difficult. However, it is not only in the field of computer science that the fast detection of vulnerabilities and correct prioritization are constantly discussed; for example, in [2][3][4], the vulnerability of buildings to seismic activity was investigated through combinations of the buildings' geometric features, social vulnerabilities, as a result of the impact of the COVID-19 pandemic, were detected [5], and impacts on areas such as critical infrastructure were assessed [6].…”