Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Harirchian, Ehsan; Lahmer, Tom; Kumari, Vandana; Jadhav, Kirti

doi:10.3390/en13133340

Cited by 33 publications

(14 citation statements)

References 34 publications

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“…Additionally, remote sensing imagery contains geospatial data in terms of the bounding box of the images and the spatial reference system; however, in this case no explicit locations or objects are identified beforehand [21]. Most commonly, remotely sensed data are used to classify damage, but also other sources of geospatial data can be used as predictors, such as information on building structures [22][23][24][25]. Harirchian, Lahmer and Rasulzade [24] developed an artificial neural network (ANN) that uses buildings' damage-inducing parameters, such as number of floors, to predict the actual observed damage.…”

Section: Previous Researchmentioning

confidence: 99%

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

et al. 2020

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Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach good performance on such a task in experimental settings. How CNNs perform when applied under operational emergency conditions, with unseen data and time constraints, is not well studied. This study focuses on the applicability of a CNN-based model in such scenarios. We performed experiments on 13 disasters that differ in natural hazard type, geographical location, and image parameters. The types of natural hazards were hurricanes, tornadoes, floods, tsunamis, and volcanic eruptions, which struck across North America, Central America, and Asia. We used 175,289 buildings from the xBD dataset, which contains human-annotated multiclass damage labels on high-resolution satellite imagery with red, green, and blue (RGB) bands. First, our experiments showed that the performance in terms of area under the curve does not correlate with the type of natural hazard, geographical region, and satellite parameters such as the off-nadir angle. Second, while performance differed highly between occurrences of disasters, our model still reached a high level of performance without using any labeled data of the test disaster during training. This provides the first evidence that such a model can be effectively applied under operational conditions, where labeled damage data of the disaster cannot be available timely and thus model (re-)training is not an option.

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Section: Previous Researchmentioning

confidence: 99%

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

et al. 2020

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“…However, the traditional classification methods usually need to establish a classification rule set [31], but human subjectivity has a great influence on the establishment of rule sets, and the selection of a large number of parameters is also time-consuming. Shallow machine learning methods such as Random Forest (RF) [36] and Support Vector Machine (SVM) [37] can achieve relatively high accuracy without setting a large number of parameters, so lots of attempts have been conducted in earthquake damage assessment [32][33][34][35][38][39][40][41]. However, the extraction of manual features from VHR images is time-consuming and requires a high level of prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

2021

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The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

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“…In another study performed by Harirchian et al [37], SVM performed comparatively efficient even in the case of many features. Kernel trick is one of SVM's strengths that blends all the knowledge required for the learning algorithm, specifying a key component in the transformed field [38].…”

Section: Background Of Studymentioning

confidence: 92%

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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Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Cited by 33 publications

References 34 publications

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

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

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