Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition

Radhika, Sudha; Tamura, Yukio; Matsui, Masahiro

doi:10.1016/j.jweia.2014.10.018

Cited by 40 publications

(14 citation statements)

References 15 publications

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“…The resulting algorithms predicted damage with an overall accuracy of 72 to 80% for simple-form buildings (such as rectangular warehouses). Radhika et al (2015) employed high-resolution, pre-and postevent satellite data for the estimation of percent damage to individual building structures, observing a correlation factor of 0.78 between automatically identified damage and manually (visually) identified damage.…”

Section: Semi-automated Damage Assessmentsmentioning

confidence: 99%

Multi-Scale Remote Sensing of Tornado Effects

Womble

Wood

Mohammadi

2018

Front. Built Environ.

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To achieve risk-based engineered structural designs that provide safety for life and property from tornadoes, sufficient knowledge of tornado wind speeds and wind flow characteristics is needed. Currently, sufficient understanding of the magnitude, frequency, and velocity structure of tornado winds remain elusive. Direct measurements of tornado winds are rare and nearly impossible to acquire, and the pursuit of in situ wind measurements can be precarious, dangerous, and even necessitating the development of safer and more reliable means to understand tornado actions. Remote-sensing technologies including satellite, aerial, lidar, and photogrammetric platforms, have demonstrated an ever-increasing efficiency for collecting, storing, organizing, and communicating tornado hazards information at a multitude of geospatial scales. Current remote-sensing technologies enable wind-engineering researchers to examine tornado effects on the built environment at various spatial scales ranging from the overall path to the neighborhood, building, and ultimately member and/or connection level. Each spatial resolution contains a unique set of challenges for efficiency, ease, and cost of data acquisition and dissemination, as well as contributions to the body of knowledge that help engineers and atmospheric scientists better understand tornado wind speeds. This paper examines the use of remote sensing technologies at four scales in recent tornado investigations, demonstrating the challenges of data collection and processing at each level as well as the utility of the information gleaned from each level in advancing the understanding of tornado effects.

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Section: Semi-automated Damage Assessmentsmentioning

confidence: 99%

Multi-Scale Remote Sensing of Tornado Effects

Womble

Wood

Mohammadi

2018

Front. Built Environ.

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“…The study made by Vitola et al have been used the KNN algorithm to examine structures and assess possible damage [22]. Radhika et al have used the SVM algorithm to detect damage to buildings by wind and tropical cyclone [23]. KNN and SVM algorithms are implemented successfully in many different areas such as health [24], indoor location detection [25], social networks [26] and image processing [27].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models

2020

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Steel fiber reinforced self-compacting concrete (SFRSCC) is a special type of concrete that is widely researched in literature due to its superior properties. As it is difficult to provide its high workability qualities, SFRSCC is thought to be in need of an economic and quick design process. In this study, it is aimed to predict the fresh properties of SFRSCC mixtures following with the standards at the preliminary design stage. With this aim, two different classification methods were applied successfully to a comprehensive dataset collected from international publications. The models used to classify the fresh performance of SFRSCC were Weighted K-Nearest Neighbors (W-KNN) and Quadratic Support Vector Machine (Q-SVM). Consequently, acceptable success rates were obtained from the models. For the prediction of slump-flow, the accuracy values were 0.76 and 0.84 for the W-KNN and Q-SVM models, respectively. For the V-funnel time, the accuracy values were 0.90 and 0.92 for the W-KNN and Q-SVM models, respectively. Owing to the recommended methods, it is expected to reduce the number of trial mixtures in the preliminary design stage of SFRSCC. INDEX TERMS Fresh properties, self-compacting concrete, steel fiber, quadratic support vector machine, weighted k-nearest neighbor.

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“…These methods present a systematic and automated way to determine statistical rules and patterns from large amounts of data. Other techniques used to study natural disaster risk include artificial neural networks [27][28][29], support vector machines [28,30], and Bayesian network and clustering. Liu et al [31] also proposed statistical model parameters for compact polarimetric synthetic aperture radar.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

Kim

Yoon

2018

Sustainability

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The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tree analysis to evaluate the risk of building damage. We generated the decision tree model to determine the risk of rain, gale, and typhoon (excluding gale with less damage). Using the weight and limit values of the weather variables derived using the decision tree model, the risk of building damage was assessed for 230 regions in South Korea until 2100. The number of regions at risk of rain damage increased by more than 30% on average. Conversely, regions at risk of damage from snowfall decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, while those at high risk of the same increased by up to 62.5% under RCP 8.5. The results of this study are highly fluid since they are based on the uncertainty of future climate change. However, the study is meaningful because it suggests a new method for assessing disaster risk using weather indices.

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Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition

Cited by 40 publications

References 15 publications

Multi-Scale Remote Sensing of Tornado Effects

Multi-Scale Remote Sensing of Tornado Effects

Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models

Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

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