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Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.
Junseo Bae; Sang-Guk Yum; Ji-Myong Kim. Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects. Sustainability 2021, 13, 6376 .
AMA StyleJunseo Bae, Sang-Guk Yum, Ji-Myong Kim. Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects. Sustainability. 2021; 13 (11):6376.
Chicago/Turabian StyleJunseo Bae; Sang-Guk Yum; Ji-Myong Kim. 2021. "Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects." Sustainability 13, no. 11: 6376.
This study goals to develop a model for predicting financial loss at construction sites using a deep learning algorithm to reduce and prevent the risk of financial loss at construction sites. Lately, as the construction of high-rise buildings and complex buildings increases and the scale of construction sites surges, the severity and frequency of accidents occurring at construction sites are swelling, and financial losses are also snowballing. Singularly, as natural disasters rise and construction projects in urban areas increase, the risk of financial loss for construction sites is mounting. Thus, a financial loss prediction model is desired to mitigate and manage the risk of such financial loss for maintainable and effective construction project management. This study reflects the financial loss incurred at the actual construction sites by collecting claim payout data from a major South Korean insurance company. A deep learning algorithm was presented in order to develop an objective and scientific prediction model. The results and framework of this study provide critical guidance on financial loss management necessary for sustainable and successful construction project management and can be used as a reference for various other construction project management studies.
Ji-Myong Kim; Junseo Bae; Seunghyun Son; Kiyoung Son; Sang-Guk Yum. Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques. Sustainability 2021, 13, 5304 .
AMA StyleJi-Myong Kim, Junseo Bae, Seunghyun Son, Kiyoung Son, Sang-Guk Yum. Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques. Sustainability. 2021; 13 (9):5304.
Chicago/Turabian StyleJi-Myong Kim; Junseo Bae; Seunghyun Son; Kiyoung Son; Sang-Guk Yum. 2021. "Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques." Sustainability 13, no. 9: 5304.
Educational facilities hold a higher degree of uncertainty in predicting maintenance and repair costs than other types of facilities. Moreover, achieving accurate and reliable maintenance and repair costs is essential, yet very little is known about a holistic approach to learning them by incorporating multi-contextual factors that affect maintenance and repair costs. This study fills this knowledge gap by modeling and validating deep neural networks to efficiently and accurately learn maintenance and repair costs, drawing on 1213 high-confidence data points. The developed model learns and generalizes claim payout records on the maintenance and repair costs from sets of facility asset information, geographic profiles, natural hazard records, and other causes of financial losses. The robustness of the developed model was tested and validated by measuring the root mean square error and mean absolute error values. This study attempted to propose an analytical modeling framework that can accurately learn various factors, significantly affecting the maintenance and repair costs of educational facilities. The proposed approach can contribute to the existing body of knowledge, serving as a reference for the facilities management of other functional types of facilities.
Jimyong Kim; Sangguk Yum; Seunghyun Son; Kiyoung Son; Junseo Bae. Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities. Buildings 2021, 11, 165 .
AMA StyleJimyong Kim, Sangguk Yum, Seunghyun Son, Kiyoung Son, Junseo Bae. Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities. Buildings. 2021; 11 (4):165.
Chicago/Turabian StyleJimyong Kim; Sangguk Yum; Seunghyun Son; Kiyoung Son; Junseo Bae. 2021. "Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities." Buildings 11, no. 4: 165.
Global warming, one of the most serious aspects of climate change, can be expected to cause rising sea levels. These, in turn, have been linked to unprecedentedly large typhoons that can cause flooding of low-lying land, coastal invasion, seawater flows into rivers and groundwater, rising river levels, and aberrant tides. To prevent loss of life and property damage caused by typhoons, it is crucial to accurately estimate storm surge related risk. This study therefore develops a statistical model for estimating probability model, based on surge data pertaining to Typhoon Maemi, which struck South Korea in 2003. Specifically, estimation of non-exceedance probability models of the typhoon-related storm surge was achieved via clustered separated peaks-over-threshold simulation, while various distribution models were fitted to the empirical data for investigating the risk of storm surge height. The result of this process found that the result of Weibull distribution was better than other distribution model for Typhoon Maemi's peak total water level.
Sang-Guk Yum; Hsi-Hsien Wei; Sung-Hwan Jang. Identifying the non-exceedance probability of extreme storm surges as a component of natural-disaster management using tidal-gauge data from Typhoon Maemi in South Korea. 2020, 2020, 1 -29.
AMA StyleSang-Guk Yum, Hsi-Hsien Wei, Sung-Hwan Jang. Identifying the non-exceedance probability of extreme storm surges as a component of natural-disaster management using tidal-gauge data from Typhoon Maemi in South Korea. . 2020; 2020 ():1-29.
Chicago/Turabian StyleSang-Guk Yum; Hsi-Hsien Wei; Sung-Hwan Jang. 2020. "Identifying the non-exceedance probability of extreme storm surges as a component of natural-disaster management using tidal-gauge data from Typhoon Maemi in South Korea." 2020, no. : 1-29.
Windstorms have caused a range of damage on the built environment. Although several risk assessment models for estimating such damage have been widely developed, the results generated by these models often turn inaccurate due to the building information required for such models at a regional scale are usually incomplete, or of a poor quality. Alternatively, this study utilizes an insurance company’s loss data pertaining to the high winds of Typhoon Maemi in South Korea in 2003 for calculating building damage in terms of damage ratios. Next, these damage ratios and storm-wind speeds are utilized for constructing vulnerability curves that can be used to predict levels of damage to designated building types subject to given wind speeds. Lastly, geographical information systems spatial data is combined with those vulnerability curves to arrive at four distinct wind-damage levels. It is hoped that the present research will serve as a reference for further studies of developing building vulnerability curves for storm winds.
Sang-Guk Yum; Ji-Myong Kim; Hsi-Hsien Wei. Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea. Journal of Building Engineering 2020, 34, 101932 .
AMA StyleSang-Guk Yum, Ji-Myong Kim, Hsi-Hsien Wei. Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea. Journal of Building Engineering. 2020; 34 ():101932.
Chicago/Turabian StyleSang-Guk Yum; Ji-Myong Kim; Hsi-Hsien Wei. 2020. "Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea." Journal of Building Engineering 34, no. : 101932.
The purpose of this research is to identify the indicators of typhoon damage and develop a metric for typhoon vulnerability functions employing the losses associated with Typhoon Maemi. Typhoons cause significant financial damages worldwide every year. Federal and local governments, insurance companies, and construction companies strive to develop typhoon risk assessment models and use them to quantify the risks so that they can avoid, mitigate, or transfer the financial risks. Therefore, typhoon risk assessment modeling is becoming increasingly important, and in order to achieve a sophisticated evaluation, it is also important to reflect more specified and local vulnerabilities. Although several previous studies on economic loss associated with natural catastrophe have identified essential risk indicators, there has been a shortage of more specific research studies focusing on the correlation between vulnerability and economic loss caused by typhoons. In order to fill this gap, this study collected and analyzed the actual loss record of Typhoon Maemi collected and accumulated by a major insurance company in Korea. In order to create the vulnerability functions and to identify the natural hazard indicators and basic building information indicators, information from the insurance record was used in the analysis. The results and metric of this research provide a pragmatic approach that helps create vulnerability functions for abovementioned sectors and like estimating local vulnerabilities and predicting and coping with the possible damage and loss from typhoons.
Ji-Myong Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn; Tiago Ferreira. Typhoon Vulnerability Analysis in South Korea Utilizing Damage Record of Typhoon Maemi. Advances in Civil Engineering 2020, 2020, 1 -10.
AMA StyleJi-Myong Kim, Kiyoung Son, Sang-Guk Yum, Sungjin Ahn, Tiago Ferreira. Typhoon Vulnerability Analysis in South Korea Utilizing Damage Record of Typhoon Maemi. Advances in Civil Engineering. 2020; 2020 ():1-10.
Chicago/Turabian StyleJi-Myong Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn; Tiago Ferreira. 2020. "Typhoon Vulnerability Analysis in South Korea Utilizing Damage Record of Typhoon Maemi." Advances in Civil Engineering 2020, no. : 1-10.
Tunnel construction, a common byproduct of rapid economic growth and transportation-system development, carries inherent risks to life and various kinds of property that operations and management professionals must take into account. Due to various and complicated geological conditions, tunnel construction projects can produce unexpected collapses, landslides, avalanches, and water-related hazards. Moreover, damage from such events can be intensified by other factors, including geological hazards caused by natural disasters, such as heavy rainfall and earthquakes, resulting in huge social, economic, and environmental losses. Therefore, the present research conducted multiple linear regression analyses on financial-loss data arising from tunnel construction in Korea to develop a novel tunnel-focused method of natural-hazard risk assessment. More specifically, the total insured value and actual value of damage to 277 tunnel-construction projects were utilized to identify significant natural-disaster indicators linked to unexpected construction-budget overruns and construction-scheduling delays. Damage ratios (i.e., actual losses over total insured project value) were used as objective, quantitative indices of the extent of damage that can be usefully applied irrespective of project size. Natural-hazard impact data—specifically wind speed, rainfall, and flood occurrences—were applied as the independent variables in the regression model. In the regression model, maximum wind speed was found to be correlated with tunnel projects’ financial losses across all three of the natural-hazard indicators. The present research results can serve as important baseline references for natural disaster-related risk assessments of tunnel-construction projects, and thus serve the wider purpose of balanced and sustainable development.
Sang-Guk Yum; Sungjin Ahn; Junseo Bae; Ji-Myong Kim. Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea. Sustainability 2020, 12, 8026 .
AMA StyleSang-Guk Yum, Sungjin Ahn, Junseo Bae, Ji-Myong Kim. Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea. Sustainability. 2020; 12 (19):8026.
Chicago/Turabian StyleSang-Guk Yum; Sungjin Ahn; Junseo Bae; Ji-Myong Kim. 2020. "Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea." Sustainability 12, no. 19: 8026.
A novel multi-functional road surface system is designed to improve safety, the efficiency of traffic flow, and environmental sustainability for future transportation systems. The surface coating, preforming temperature detection with heating element and hydrophobic features, were fabricated with a nanocomposite consisting of carbon nanotube (CNT) modified polyurethane (PU). The CNT/PU coating showed higher electrical conductivity as well as enhanced hydrophobic properties as the CNT concentration increased. The multifunctional properties of CNT/PU coatings were investigated for use in freezing temperature sensing and heating. The CNT/PU coatings showed high temperature sensitivity in the freezing temperature range with a negative temperature coefficient of resistance. In addition, the CNT/PU coatings had excellent heating performance due to the Joule heating effect. Therefore, the proposed CNT/PU coatings are promising for use as multifunctional road coating materials for detection of freezing temperature and deicing by self-heating.
Sang-Guk Yum; Huiming Yin; Sung-Hwan Jang. Toward Multi-Functional Road Surface Design with the Nanocomposite Coating of Carbon Nanotube Modified Polyurethane: Lab-Scale Experiments. Nanomaterials 2020, 10, 1905 .
AMA StyleSang-Guk Yum, Huiming Yin, Sung-Hwan Jang. Toward Multi-Functional Road Surface Design with the Nanocomposite Coating of Carbon Nanotube Modified Polyurethane: Lab-Scale Experiments. Nanomaterials. 2020; 10 (10):1905.
Chicago/Turabian StyleSang-Guk Yum; Huiming Yin; Sung-Hwan Jang. 2020. "Toward Multi-Functional Road Surface Design with the Nanocomposite Coating of Carbon Nanotube Modified Polyurethane: Lab-Scale Experiments." Nanomaterials 10, no. 10: 1905.
Extensive use has been made of lifecycle-cost assessment to enhance the cost-effectiveness and resilience of facilities management. However, if such assessments are to be truly effective, supplemental information will be needed on the major costs to be expected over buildings’ entire lives. Electricity generation and distribution systems, for example, are absolutely indispensable to industry and human society, not least in the operation of buildings and other infrastructure as networks. The widespread disruption that ensues when such power systems are damaged often carries considerable repair costs. Natural disasters likewise can cause extensive societal, economic, and environmental damage. Such damage is often associated with lengthy power outages that, as well as being directly harmful, can hinder emergency response and recovery. Accordingly, the present study investigated the correlations of natural hazard indicators such as wind speed and rainfall, along with environmental data regarding the power failure in Florida caused by Hurricane Irma in 2017 utilizing multiple regression analysis. The environmental data in question, selected on the basis of a thorough literature review, was tree density. Our analysis indicated that the independent variables, maximum wind speed, total rainfall, and tree density, were all significantly correlated with the dependent variable, power failure. Among these, rainfall was the least significant. Despite there being only three independent variables in the model, its adjusted coefficient of determination (0.512) indicated its effectiveness as a predictor of the power outages caused by Hurricane Irma. As such, our results can serve the construction industry’s establishment of advanced safety guidelines and structural designs power transmission systems in regions at risk of hurricanes and typhoons. Additionally, insurance companies’ loss-assessment modeling for power-system facilities would benefit from incorporating the three identified risk indicators. Finally, our findings can serve as a useful reference to policymakers tasked with mitigating power outages’ effects on infrastructure in hurricane-prone areas. It is hoped that this work will be extended, facilitating infrastructure restoration planning and making societies and economies more sustainable.
Sang-Guk Yum; Kiyoung Son; Seunghyun Son; Ji-Myong Kim. Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma. Sustainability 2020, 12, 7702 .
AMA StyleSang-Guk Yum, Kiyoung Son, Seunghyun Son, Ji-Myong Kim. Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma. Sustainability. 2020; 12 (18):7702.
Chicago/Turabian StyleSang-Guk Yum; Kiyoung Son; Seunghyun Son; Ji-Myong Kim. 2020. "Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma." Sustainability 12, no. 18: 7702.
This study analyzed the relative risks of migrant workers, and identified risk factors based on quantitative data for the systematic safety management of migrant workers. Many studies have found that migrant workers are more vulnerable to safety accidents than non-migrant workers. Nevertheless, there are few quantitative studies of migrant workers’ accident-risk in the construction industry, where safety accidents are most frequent. In addition, safety management for the identified accident risk factors has not been implemented systematically. To fill the gap, this study uses safety accident data from construction sites, from the +, for the methodical safety management of migrant workers. The t-test and multiple regression analysis methods are used to define the variance in non-migrant and migrant workers, and the risk indicators, respectively. The two analyses show that the results for migrant construction workers were 2.2% higher in safety accident severity than non-migrant workers, and significant factors are also different. This study’s results will provide critical guidance for the safety management of migrant construction workers.
Ji-Myong Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn. Analyzing the Risk of Safety Accidents: The Relative Risks of Migrant Workers in Construction Industry. Sustainability 2020, 12, 5430 .
AMA StyleJi-Myong Kim, Kiyoung Son, Sang-Guk Yum, Sungjin Ahn. Analyzing the Risk of Safety Accidents: The Relative Risks of Migrant Workers in Construction Industry. Sustainability. 2020; 12 (13):5430.
Chicago/Turabian StyleJi-Myong Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn. 2020. "Analyzing the Risk of Safety Accidents: The Relative Risks of Migrant Workers in Construction Industry." Sustainability 12, no. 13: 5430.
To optimally maintain buildings and other built infrastructure, the costs of managing them during their entire existence—that is, lifecycle costs—must be taken into account. However, due to technological improvements, developers now build more high-rise and high-performance buildings, meaning that new approaches to estimating lifecycle costs are needed. Meanwhile, an accelerating process of industrialization around the world means that global warming is also accelerating, and the damage caused by natural disasters due to climate change is increasing. However, the costs of losses related to such hazards are rarely incorporated into lifecycle-cost estimation techniques. Accordingly, this study explored the relationship between, on the one hand, some known parameters of natural disasters, such as earthquakes, high winds, and/or flooding, and on the other hand, the data on exceptional maintenance costs, represented by gross loss costs, generated by a large international hotel chain from 2007 to 2017. The regression model used revealed a correlation between heavy rain and insurance-claim payouts. This and other results can usefully inform safety and design guidelines for policymakers, both in disaster management and real estate, as well as in insurance companies
Sang-Guk Yum; Ji-Myong Kim; Kiyoung Son. Natural Hazard Influence Model of Maintenance and Repair Cost for Sustainable Accommodation Facilities. Sustainability 2020, 12, 4994 .
AMA StyleSang-Guk Yum, Ji-Myong Kim, Kiyoung Son. Natural Hazard Influence Model of Maintenance and Repair Cost for Sustainable Accommodation Facilities. Sustainability. 2020; 12 (12):4994.
Chicago/Turabian StyleSang-Guk Yum; Ji-Myong Kim; Kiyoung Son. 2020. "Natural Hazard Influence Model of Maintenance and Repair Cost for Sustainable Accommodation Facilities." Sustainability 12, no. 12: 4994.
Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.
Zhenyu Zhang; Hsi-Hsien Wei; Sang Guk Yum; Jieh-Haur Chen. Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Applied Sciences 2019, 9, 3915 .
AMA StyleZhenyu Zhang, Hsi-Hsien Wei, Sang Guk Yum, Jieh-Haur Chen. Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Applied Sciences. 2019; 9 (18):3915.
Chicago/Turabian StyleZhenyu Zhang; Hsi-Hsien Wei; Sang Guk Yum; Jieh-Haur Chen. 2019. "Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method." Applied Sciences 9, no. 18: 3915.
Typhoons cause severe monetary damage globally. Many global insurance companies and public agencies are currently developing and utilizing windstorm risk estimation models to calculate the level of risk and set up strategies for avoiding, mitigating, and relocating those economic risks. Hence, the usage and accuracy of the windstorm risk estimation model is becoming increasingly significant, and reflecting local vulnerabilities is essential for refined risk assessment. While key risk indicators have been recognized in practical studies of economic losses associated with windstorms, there remains a lack of comprehensive research addressing the relationship between economic losses of residential buildings for South Korea and vulnerability. This research investigates the real damage record of Typhoon Maemi from an insurance company in order to bridge this gap. The aim of this study is to define the damage indicators of typhoons and create a framework for typhoon damage function, using the damage caused by Typhoon Maemi as a representative paradigm. Basic building information and natural disaster indicators are adopted to develop the damage function. The results and metric of this research provide a pragmatic approach that helps create damage functions for insurance companies and contingency planners, reflecting the actual financial losses and local vulnerabilities of buildings. The framework and results of this study will provide a practical way to manage extreme cases of natural disasters, develop a damage function for insurers and public authorities, and reveal the real economic damage and local vulnerability of residential buildings in South Korea.
Ji-Myong Kim; Taehui Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn. Measuring Vulnerability of Typhoon in Residential Facilities: Focusing on Typhoon Maemi in South Korea. Sustainability 2019, 11, 2768 .
AMA StyleJi-Myong Kim, Taehui Kim, Kiyoung Son, Sang-Guk Yum, Sungjin Ahn. Measuring Vulnerability of Typhoon in Residential Facilities: Focusing on Typhoon Maemi in South Korea. Sustainability. 2019; 11 (10):2768.
Chicago/Turabian StyleJi-Myong Kim; Taehui Kim; Kiyoung Son; Sang-Guk Yum; Sungjin Ahn. 2019. "Measuring Vulnerability of Typhoon in Residential Facilities: Focusing on Typhoon Maemi in South Korea." Sustainability 11, no. 10: 2768.