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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.
This study aims to quantify the losses to third-parties on construction sites by determining the loss indicators and identifying the relationship between the losses and the indicators to improve the sustainability on building construction sites. The growing size and intricacy of recent construction projects have resulted in the growth of losses, both in quantity and frequency. Notably, third-party losses are rapidly increasing owing to the urbanization of the environment and increases in construction scale. Therefore, for efficient and sustainable construction management, a financial loss assessment model is essential to mitigate and manage such loss. This study uses the third-party losses on construction sites obtained from a major South Korean insurance company to describe the difference from the material losses and to disclose the loss indicators based on actual economic losses. ANOVA analysis and multiple regression analysis are adopted to identify the variance and define the loss indicators and to make prediction models, respectively. Several groups of loss indicators are investigated, including construction information and the occurrence of natural disasters. The findings and results of this research afford an essential guide to sustainable construction management, and they can serve as a first stage loss assessment model for construction projects.
Ji-Myong Kim; Kag-Cheon Ha; Sungjin Ahn; Seunghyun Son; Kiyoung Son. Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea. Sustainability 2020, 12, 10153 .
AMA StyleJi-Myong Kim, Kag-Cheon Ha, Sungjin Ahn, Seunghyun Son, Kiyoung Son. Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea. Sustainability. 2020; 12 (23):10153.
Chicago/Turabian StyleJi-Myong Kim; Kag-Cheon Ha; Sungjin Ahn; Seunghyun Son; Kiyoung Son. 2020. "Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea." Sustainability 12, no. 23: 10153.
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.
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.
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.
In recent years, natural disasters and climate abnormalities have increased worldwide. The Fifth Assessment Report (2014) of the Intergovernmental Panel on Climate Change warned of extreme rainfall events, warming and acidification, global mean temperature rises, and average sea level rises. In many countries, changes in weather disaster patterns, such as typhoons and heavy rains, have already led to increased damage to buildings. However, the empirical quantification of typhoon risk and building damage due to climate change is insufficient. The purpose of this study was to quantify the risk of building loss from typhoon pattern change caused by climate change. To this end, the intensity and frequency of typhoons affecting Korea were analyzed to examine typhoon patterns. In addition, typhoon risk was quantified using the Korean typhoon vulnerability function utilized by insurers, reinsurers, and vendors, the major users of catastrophe modeling. Hence, through this study, it is possible to generate various risk management strategies, which can be used by governments when establishing climate change policies and help insurers to improve their business models through climate risk assessment based on reasonable quantitative typhoon damage scenarios.
Ji-Myong Kim; Seunghyun Son; Sungho Lee; Kiyoung Son. Cost of Climate Change: Risk of Building Loss from Typhoon in South Korea. Sustainability 2020, 12, 7107 .
AMA StyleJi-Myong Kim, Seunghyun Son, Sungho Lee, Kiyoung Son. Cost of Climate Change: Risk of Building Loss from Typhoon in South Korea. Sustainability. 2020; 12 (17):7107.
Chicago/Turabian StyleJi-Myong Kim; Seunghyun Son; Sungho Lee; Kiyoung Son. 2020. "Cost of Climate Change: Risk of Building Loss from Typhoon in South Korea." Sustainability 12, no. 17: 7107.
The Korean construction industry has attracted interest and investment demand for lease-oriented investment products, such as shopping malls and studio apartments, as a substitute for financial products because of the low interest rates of the banks that resulted from the economic recession after the global financial crisis in 2008. However, there have been huge economic damages because of problems such as the oversupply, the increase in the unsold presale rate, and the decrease in rental profit. For studio-apartment development projects, dynamic analysis should be applied considering the correlation of variables in business analysis, which is complicated by such factors as profit structure and money flow. Therefore, we aim in this study to develop a statistical analysis model of studio apartments using probabilistic estimation. For this purpose, we developed a causal-loop diagram and established a simulation and optimization model. The developed model was verified by applying it to actual cases. Our results can be used as a reference for the optimization and risk management of studio-apartment business analysis in academia. In addition, from a practical point of view, this model can be used to develop a forecasting feasibility study based on risk and for business feasibility analysis.
Ji-Myong Kim; Kiyoung Son; Junho Jang; Seunghyun Son. Development of an income and cost simulation model for studio apartment using probabilistic estimation. Journal of Asian Architecture and Building Engineering 2020, 1 -10.
AMA StyleJi-Myong Kim, Kiyoung Son, Junho Jang, Seunghyun Son. Development of an income and cost simulation model for studio apartment using probabilistic estimation. Journal of Asian Architecture and Building Engineering. 2020; ():1-10.
Chicago/Turabian StyleJi-Myong Kim; Kiyoung Son; Junho Jang; Seunghyun Son. 2020. "Development of an income and cost simulation model for studio apartment using probabilistic estimation." Journal of Asian Architecture and Building Engineering , no. : 1-10.
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.
Bridges are important infrastructures for urban growth and the economic development of a country, because bridges allow a large volume of logistics and transportation by connecting rivers, canyons, islands and lands. As such, massive resources including financial, material and human resources are invested for bridge construction and management. However, although the latest bridge construction is undergoing rapid development of new technologies and designs, the management and prevention of risks still tend to rely on qualitative practices, which, as a result, calls for more quantified and systematic measurement and, thus, more sustainable management of potential risks. As part of efforts in managing risks to achieve quantitative risk management, this study aimed to predict losses of financial resources by identifying statistically significant risk factors based on the past record of insurance claim payouts (compensation for a loss that occurred as a result of a material damage in bridge construction projects) from a major insurance company in Korea, and conducted a multiple regression analysis to identify the loss indicators and to develop a loss estimation model. The statistical analysis confirmed that superstructure types, superstructure construction methods, and construction duration are the three significant risk factors that affects financial losses of bridge construction projects among the seven variables adopted as independent variables, which included the superstructure type, maximum span length, superstructure construction method, foundation type, floods, typhoons, and construction duration. Such findings, and the consequentially developed risk prediction model of this study, will contribute to sustainable construction management through cost reduction by predicting and preventing the future financial loss factors of bridge construction.
Ji-Myong Kim; Taehui Kim; Sungjin Ahn. Loss Assessment for Sustainable Industrial Infrastructure: Focusing on Bridge Construction and Financial Losses. Sustainability 2020, 12, 5316 .
AMA StyleJi-Myong Kim, Taehui Kim, Sungjin Ahn. Loss Assessment for Sustainable Industrial Infrastructure: Focusing on Bridge Construction and Financial Losses. Sustainability. 2020; 12 (13):5316.
Chicago/Turabian StyleJi-Myong Kim; Taehui Kim; Sungjin Ahn. 2020. "Loss Assessment for Sustainable Industrial Infrastructure: Focusing on Bridge Construction and Financial Losses." Sustainability 12, no. 13: 5316.
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.
Due to the recent introduction of innovative construction methods and technologies, construction projects increasingly require sustainability in their high degrees of specialization and complex work processes. This is due to a wide variety of new risk factors associated with construction projects that can lead to extensive and severe damage. When an accident occurs during a construction project, it can cause material, property, or bodily damage not only within the actual construction site but also outside, affecting third parties. This study analyzed the record of such third-party damage and the subsequent financial losses in bridge construction management, to identify the objective and quantified relationship of risk indicators related to the damage and losses. In order to assess the actual losses in construction projects, we adopted the loss claim payout data as recorded and provided by a major Korean insurance company, and conducted a multiple regression analysis to identify the loss indicators and to develop a loss estimation model. In this study, the analysis of the data indicated that the superstructure type, the foundation type, floods, and company ranking by the amount of the contract were the four statistically significant risk indicators that affected financial losses from third-party damage, among the nine variables used as independent variables, which included the superstructure type, foundation type, superstructure construction method, maximum span length, floods, typhoons, total construction cost, total construction period, and company ranking. As this study focused on identifying the risk factors and producing a loss assessment model quantified in numerical values, the results provide important references for assessing and minimizing the risks to third parties and the consequential financial losses in bridge construction, while promoting sustainability objectives.
Sungjin Ahn; Taehui Kim; Ji-Myong Kim. Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction. Sustainability 2020, 12, 3435 .
AMA StyleSungjin Ahn, Taehui Kim, Ji-Myong Kim. Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction. Sustainability. 2020; 12 (8):3435.
Chicago/Turabian StyleSungjin Ahn; Taehui Kim; Ji-Myong Kim. 2020. "Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction." Sustainability 12, no. 8: 3435.
Construction worker safety and safety training continue to be the main issues in the construction industry. As a means of improving construction worker safety, this study focuses on safety training at an actual construction worksite. In order to promote safety awareness among workers, it is imperative to develop more effective safety training. This study examined safety training as a method of improving construction worker safety, focusing on the effectiveness of the instructional delivery method. Effectiveness pertains to level of understanding of instruction and can be enhanced through improving instructional delivery method. This study aims to examine two different types of safety training methods: (1) the conventional lecture method and (2) innovative method using the 3D Building Information Modeling (BIM) simulation, reflecting the hazard condition of the actual site. An experiment is conducted, in which the two types of training are implemented and assessed through testing trainees’ understanding. The workers trained via BIM simulation showed a higher level of understanding than the group of workers who were trained conventionally. Also, a survey was conducted targeting safety managers, in which the workers evaluated lifelike quality of the training, active learning and enjoyment that each of the training methods can promote. This research will provide implications that an innovative method using the virtual reality is more effective than the conventional lecture method.
Sungjin Ahn; Taehui Kim; Young-Jun Park; Ji-Myong Kim. Improving Effectiveness of Safety Training at Construction Worksite Using 3D BIM Simulation. Advances in Civil Engineering 2020, 2020, 1 -12.
AMA StyleSungjin Ahn, Taehui Kim, Young-Jun Park, Ji-Myong Kim. Improving Effectiveness of Safety Training at Construction Worksite Using 3D BIM Simulation. Advances in Civil Engineering. 2020; 2020 ():1-12.
Chicago/Turabian StyleSungjin Ahn; Taehui Kim; Young-Jun Park; Ji-Myong Kim. 2020. "Improving Effectiveness of Safety Training at Construction Worksite Using 3D BIM Simulation." Advances in Civil Engineering 2020, no. : 1-12.
Countries around the world are making efforts to develop and introduce green building certification systems to save energy and reduce greenhouse gas emissions. As a result of these efforts, green certification systems are rapidly spreading. Consistent with this, certification systems are also being developed and research related to various technologies and regulations is ongoing. However, most research focuses on residential and commercial buildings and there is still a lack of scientific research on educational facilities. To fill the gap and support the former studies, this research statistically studies the economic effects of green certification systems on educational facilities. For this purpose, the benefits, i.g., building price and maintenance & repair costs, were examined for universities in Canada that were admitted to the Canadian Educational Institution. As shown by the results of this study, Leadership in Energy and Environmental Design (LEED)-certified buildings cost 49.9% more to build and had 25.6% lower maintenance and repair costs than non-LEED certified buildings.
Ji-Myong Kim; Kiyoung Son; Seunghyun Son. GREEN BENEFITS ON EDUCATIONAL BUILDINGS ACCORDING TO THE LEED CERTIFICATION. International Journal of Strategic Property Management 2020, 24, 83 -89.
AMA StyleJi-Myong Kim, Kiyoung Son, Seunghyun Son. GREEN BENEFITS ON EDUCATIONAL BUILDINGS ACCORDING TO THE LEED CERTIFICATION. International Journal of Strategic Property Management. 2020; 24 (2):83-89.
Chicago/Turabian StyleJi-Myong Kim; Kiyoung Son; Seunghyun Son. 2020. "GREEN BENEFITS ON EDUCATIONAL BUILDINGS ACCORDING TO THE LEED CERTIFICATION." International Journal of Strategic Property Management 24, no. 2: 83-89.
There are many risks and uncertainties in plant construction projects, because of their complexity, difficulty in loss prediction and size of construction being large. The risk management of such plant construction projects should not be relied solely on the experiences and intuition of the contractors or the construction managers as it has been in the past. For that reason, a new quantitative and empirical risk analysis is required, in order for the development of a+65 risk assessment using risk indicators for the plant construction projects. This research used the insurance payout record from a global insurance company to reflect the actual quantitative loss in the risk assessment model for plant construction project. The researchers adopted the geographic information as well as construction information, (construction phase and commissioning phase, process rate, total duration) as the independent variables, which found to be statistically significant in the analysis in this study. In the analysis, in which the researchers adopted the geographic information and construction information as the independent variables, it was found that, as the relationship between damage ratio and the valid variables were identified as statistically significant, the damage function model is statistically significant. This research suggests that the regression model containing such valid independent variables could be beneficial in terms of providing foundational guidelines for the plant construction project risk analysis.
Ji-Myong Kim; Taehui Kim; Junseo Bae; Kiyoung Son; Sungjin Ahn. Analysis of plant construction accidents and loss estimation using insurance loss records. Journal of Asian Architecture and Building Engineering 2019, 18, 507 -516.
AMA StyleJi-Myong Kim, Taehui Kim, Junseo Bae, Kiyoung Son, Sungjin Ahn. Analysis of plant construction accidents and loss estimation using insurance loss records. Journal of Asian Architecture and Building Engineering. 2019; 18 (6):507-516.
Chicago/Turabian StyleJi-Myong Kim; Taehui Kim; Junseo Bae; Kiyoung Son; Sungjin Ahn. 2019. "Analysis of plant construction accidents and loss estimation using insurance loss records." Journal of Asian Architecture and Building Engineering 18, no. 6: 507-516.
The purpose of this study is to suggest a quantitative risk assessment approach for construction sites using risk indicators to predict economic damages. The frequency of damage in building construction has recently increased, and the associated costs have been increased as well. Although a request for a damage estimation model has been extended, the industry still lacks quantitative and comprehensive research that reveals the physical relationship between damage and risk indicators. To address that issue, we use an insurance company’s payouts from construction site claims in South Korea to reflect the real financial damage. We adopted a multiple regression method to define the risk indicators: geographic vulnerability, natural hazards, capability, and general project information. The results and findings of this research will be accepted as an essential guideline for developing a construction risk estimation model.
Ji-Myong Kim; Taehui Kim; Kiyoung Son; Junseo Bae; Seunghyun Son. A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea. Journal of Asian Architecture and Building Engineering 2019, 18, 472 -478.
AMA StyleJi-Myong Kim, Taehui Kim, Kiyoung Son, Junseo Bae, Seunghyun Son. A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea. Journal of Asian Architecture and Building Engineering. 2019; 18 (5):472-478.
Chicago/Turabian StyleJi-Myong Kim; Taehui Kim; Kiyoung Son; Junseo Bae; Seunghyun Son. 2019. "A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea." Journal of Asian Architecture and Building Engineering 18, no. 5: 472-478.
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.
This study identifies the risk indicators of building damage from typhoons and determines the correlations among this damage, typhoon information, geographic vulnerability, construction environment, and socioeconomic vulnerability. This fundamental research aids the development of a typhoon loss prediction model for building construction projects in South Korea. Extreme weather events have become increasingly prevalent around the world, with subsequent increases in related damages. Early attempts to meet the growing demands for a loss prediction model have been insufficiently comprehensive, and specifically in South Korea, research on risk indicators is needed that considers the geographic, building, and socioeconomic features. This research used the regional typhoon loss records from the annual report of the Ministry of Public Safety and Security (MPSS) to define the dependent variable of building damage. The results and findings of this study will inform the development of a typhoon loss prediction model in South Korea.
Ji-Myong Kim; Kiyoung Son; Youngmi Yoo; Donghoon Lee; Dae Young Kim. Identifying Risk Indicators of Building Damage Due to Typhoons: Focusing on Cases of South Korea. Sustainability 2018, 10, 3947 .
AMA StyleJi-Myong Kim, Kiyoung Son, Youngmi Yoo, Donghoon Lee, Dae Young Kim. Identifying Risk Indicators of Building Damage Due to Typhoons: Focusing on Cases of South Korea. Sustainability. 2018; 10 (11):3947.
Chicago/Turabian StyleJi-Myong Kim; Kiyoung Son; Youngmi Yoo; Donghoon Lee; Dae Young Kim. 2018. "Identifying Risk Indicators of Building Damage Due to Typhoons: Focusing on Cases of South Korea." Sustainability 10, no. 11: 3947.