• Wind and Structures
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• v.13 no.2
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• pp.191-206
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• 2010

Windborne debris is a major cause of structural damage during severe windstorms and hurricanes owing to its direct impact on building envelopes as well as to the 'chain reaction' failure mechanism it induces by interacting with wind pressure damage. Estimation of debris risk is an important component in evaluating wind damage risk to residential developments. A debris risk model developed by the authors enables one to analytically aggregate damage threats to a building from different types of debris originating from neighboring buildings. This model is extended herein to a general debris risk analysis methodology that is then incorporated into a vulnerability model accounting for the temporal evolution of the interaction between pressure damage and debris damage during storm passage. The current paper (Part I) introduces the debris risk analysis methodology, establishing the mathematical modeling framework. Stochastic models are proposed to estimate the probability distributions of debris trajectory parameters used in the method. It is shown that model statistics can be estimated from available information from wind-tunnel experiments and post-damage surveys. The incorporation of the methodology into vulnerability modeling is described in Part II.

• Earthquakes and Structures
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• v.4 no.3
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• pp.299-324
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• 2013

The current research focuses on the seismic vulnerability assessment of typical Southern Europe buildings, based on processing of a large set of observational damage data. The presented study constitutes a sequel of a previous research. The damage statistics have been enriched and a wider damage database (178578 buildings) is created compared to the one of the first presented paper (73468 buildings) with Damage Probability Matrices (DPMs) after the elaboration of the results from post-earthquake surveys carried out in the area struck by the 7-9-1999 near field Athens earthquake. The dataset comprises buildings which developed damage in several degree, type and extent. Two different parameters are estimated for the description of the seismic demand. After the classification of damaged buildings into structural types they are further categorized according to the level of damage and macroseismic intensity. The relative and the cumulative frequencies of the different damage states, for each structural type and each intensity level, are computed and presented, in terms of damage ratio. Damage Probability Matrices (DPMs) are obtained for typical structural types and they are compared to existing matrices derived from regions with similar building stock and soil conditions. A procedure is presented for the classification of those buildings which initially could not be discriminated into structural types due to restricted information and hence they had been disregarded. New proportional DPMs are developed and a correlation analysis is fulfilled with the existing vulnerability relations.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.668-675
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• 2018

The continuing urbanization and industrialization around the world has required a large amount of power. Therefore, construction of major infrastructure, including nuclear power plants in coastal areas, has accelerated. In addition, the intensity of natural disasters is increasing due to global warming and abnormal climate phenomena. Natural disasters are difficult to predict in terms of occurrence, location, and scale, resulting in human casualties and property damage. For these reasons, the disaster scale and damage estimation in coastal areas have become important issues. The present study examined the predictable weather data and regional ratings and developed estimating functions for wind wave damage based on the disaster statistics in the southern areas. The results of the present study are expected to help disaster management in advance of the wind wave damage. The NRMSE was used for verification. The accuracy of the NRMSE results ranged from 1.61% to 21.73%.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.190-203
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• 2015

VRI(Vulnerability-Resilience Index), which is defined as a function of 3 variables: climate exposure, sensitivity, and adaptive capacity, has been quantified for the case of Typhoon which is one of the extreme weathers that will become more serious as climate change proceeds. Because VRI is only indicating the relative importance of vulnerability between regions, the VRI quantification is prerequisite for the effective adaptation policy for climate in Korea. For this purpose, damage statistics such as amount of damage, occurrence frequency, and major damaged districts caused by Typhoon over the past 20 years, has been employed. According to the VRI definition, we first calculated VRI over every district in the case of both with and without weighting factors of climate exposure proxy variables. For the quantitative estimation of weighting factors, we calculated correlation coefficients (R) for each of the proxy variables against damage statistics of Typhoon, and then used R as weighting factors of proxy variables. The results without applying weighting factors indicates some biases between VRI and damage statistics in some regions, but most of biases has been improved by applying weighting factors. Finally, due to the relations between VRI and damage statistics, we are able to quantify VRI expressed as a unit of KRW, showing that VRI=1 is approximately corresponding to 500 hundred million KRW. This methodology of VRI quantification employed in this study, can be also practically applied to the number of future climate scenario studies over Korea.

• Journal of Fashion Business
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• v.18 no.1
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• pp.149-163
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• 2014

The purpose of this study is to investigate and analyze the actual conditions of consumer damage occuring in the use of clothing products. The data used for analysis included 470 cases, which were deliberated by requesting consumer disputes deliberation at the consumer consultation room of Masan YWCA at the Kyeongsangnamdo Consumer Life Center belonging to the Kyeongnam provincial office. The disputes regarding the clothing products insisted that consumers suffered damage for the period from March, 2011 to June, 2013. The data processing was carried out by SPSS 14 and the statistics techniques used went through a cross tabulation analysis and ${\chi}^2$-test. The results are as follows. The difference in the analysis result of purchase path and material as to kinds of clothing products showed a significant difference. The damage types of clothing products were classified into five types: change of color, change of style, change of surface and touch, breakage of subsidiary materials, and others. The damaged clothing products showed a difference for damage frequency according to the items of clothing products; in particular, damage frequency for change of color appeared high. The damage contents of change of color were identified as metachromatism, discoloration and yellowing, stain occurrence, and decolorization. The damage responsibility for these clothing products appeared to be various as to clothing items, but was higher at dry cleaners and manufacturers.

• Earthquakes and Structures
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• v.26 no.2
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• pp.87-96
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• 2024

This paper proposes a logistic multinomial regression approach to model the spatial cross-correlation of damage probabilities among different damage states in an expanded transportation network. Utilizing Bayesian theory and the multinomial logistic model, we analyze the damage states and probabilities of bridges while incorporating damage correlation. This correlation is considered both between bridges in a network and within each bridge's damage states. The correlation model of damage probabilities is applied to the seismic assessment of a portion of Tehran's transportation network, encompassing 26 bridges. Additionally, we introduce extra daily traffic time (EDTT) as an operational parameter of the transportation network and employ the shortest path algorithm to determine the path between two nodes. Our results demonstrate that incorporating the correlation of damage probabilities reduces the travel time of the selected network. The average decrease in travel time for the correlated case compared to the uncorrelated case, using two selected EDTT models, is 53% and 71%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.69-75
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon that occurs worldwide. Especially, damage caused by natural disasters in coastal areas around the world such as Earthquake in Japan, Hurricane Katrina in the United States, and Typhoon Maemi in Korea are huge. If we can predict the damage scale in response to disasters, we can respond quickly and reduce damage. In this study, we developed damage prediction functions for Wind waves caused by sea breezes and waves during various natural disasters. The disaster report (1991 ~ 2017) has collected the history of storm and typhoon damage in coastal areas in Korea, and the amount of damage has been converted as of 2017 to reflect inflation. In addition, data on marine weather factors were collected in the event of storm and typhoon damage. Regression analysis was performed through collected data, Finally, predictive function of the sea turbulent damage by the sea area in 74 regions of the country were developed. It is deemed that preliminary damage prediction can be possible through the wind damage prediction function developed and is expected to be utilized to improve laws and systems related to disaster statistics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.14-22
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• 2017

The frequency and scale of natural disasters due to the abnormal climate phenomena caused by global warming have being increasing all over the world. Various natural disasters, such as typhoons, earthquakes, floods, heavy rain, drought, sweltering heat, wind waves, tsunamis and so on, can cause damage to human life. Especially, the damage caused by natural disasters such as the Earthquake of Japan, hurricane Katrina in the United States, typhoon Maemi and so on, have been enormous. At this stage, it is difficult to estimate the scale of damage due to (future) natural disasters and cope with them. However, if we could predict the scale of damage at the disaster response level, the damage could be reduced by responding to them promptly. In the present study, therefore, among the many types of natural disaster, we developed a function to estimate the damage due to wind waves caused by sea winds and waves. We collected the damage records from the Disaster Report ('91~'14) published by the Ministry of Public Safety and Security about wind waves and typhoons in the western coastal zone and, in order to reflect the inflation rate, we converted the amount of damage each year into the equivalent amount in 2014. Finally, the meteorological data, such as the wave height, wind speed, tide level, wave direction, wave period and so on, were collected from the KMA (Korea Meteorological Administration) and KHOA (Korea Hydrographic and Oceanographic Agency)'s web sites, for the periods when wind wave and typhoon damage occurred. After that, the function used to estimate the wind wave damage was developed by reflecting the regional characteristics for the 9 areas of the western coastal zone.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.315-318
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• 2007

In order to reduce the amount of damage from natural disasters, we needs prevention meteorological database classified into the cause of disaster, damage elements etc. For this, we have analyzed four data, such as Statistical yearbook of calamities issued by the National Emergency Management Agency and Annual Climatological Report issued by the Korea Meteorological Administration and Recently 10 years for natural disaster damage and Statistics Yearbook from the Ministry of Government Administration and Human affairs. Through the analysis of disaster data, we have selected input variables, such as causes and elements, occurrence frequencies, vulnerable areas of natural disaster, etc. In order to reduce damage from natural disaster, the prevention activities and forecasting based on meteorological parameters and damage datas are required. In addition, it is necessary to process meteorological information for disaster prevention activities. Through these procedure, we have established the foundation of database about natural disasters. This database will be used to assess the natural disasters and build risk model and natural disasters mitigation plan.