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Junseo Bae
Lecturer in Construction Management, School of Computing, Engineering and Physical Sciences, Univ. of the West of Scotland, Henry Buildings South, E265, High St., Paisley PA1 2BE, UK. ORCID:

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Journal article
Published: 03 June 2021 in Sustainability
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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.

ACS Style

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 Style

Junseo 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 Style

Junseo 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.

Journal article
Published: 10 May 2021 in Sustainability
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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.

ACS Style

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 Style

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 (9):5304.

Chicago/Turabian Style

Ji-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.

Journal article
Published: 15 April 2021 in Buildings
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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.

ACS Style

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 Style

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 (4):165.

Chicago/Turabian Style

Jimyong 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.

Journal article
Published: 28 September 2020 in Sustainability
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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.

ACS Style

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 Style

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 (19):8026.

Chicago/Turabian Style

Sang-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.

Research article
Published: 03 September 2020 in Environment and Planning B: Urban Analytics and City Science
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Level-of-service has been widely used to measure the operational efficiency of existing highway systems categorically, based on certain ranges of traffic speeds. However, this existing method is generic for investigating urban traffic characteristics. Hence, there is a crucial knowledge gap in capturing the unique traffic speed conditions during a certain temporal duration, in a common spatial area that includes different land use clusters. This study fills this gap by modeling the link between traffic speeds and land use clusters during certain time periods, along with the given level-of-service criteria. As a case study, this study adopted the central business district in Los Angeles in the United States. A total of 1780 traffic sensor speed data on Interstate 10 East adjacent to the central business district of Los Angeles was collected and clustered by the land use designated by the zoning regulations of the city of Los Angeles. The proposed traffic time–speed curve model that integrates different land uses in a large urban core was then developed and validated statistically, using historical real-world traffic data. Finally, an illustrative example was presented to demonstrate how the proposed model can be implemented to measure critical time periods and corresponding speeds per land-use cluster, responding to the designated level-of-service criteria. This study focused on making recommendations for government transportation agencies to employ an appropriate method that can estimate critical time periods affecting the existing operational status of a highway segment in different land-use clusters within a common spatial area, while promoting an effective application of a set of traffic sensor speed data.

ACS Style

Junseo Bae; Kunhee Choi. A land-use clustering approach to capturing the level-of-service of large urban corridors: A case study in downtown Los Angeles. Environment and Planning B: Urban Analytics and City Science 2020, 1 .

AMA Style

Junseo Bae, Kunhee Choi. A land-use clustering approach to capturing the level-of-service of large urban corridors: A case study in downtown Los Angeles. Environment and Planning B: Urban Analytics and City Science. 2020; ():1.

Chicago/Turabian Style

Junseo Bae; Kunhee Choi. 2020. "A land-use clustering approach to capturing the level-of-service of large urban corridors: A case study in downtown Los Angeles." Environment and Planning B: Urban Analytics and City Science , no. : 1.

Journal article
Published: 17 June 2020 in Transportation Planning and Technology
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ACS Style

Junseo Bae; Kunhee Choi. A new perspective for precision evaluation of large-scale traffic sensor data measurement. Transportation Planning and Technology 2020, 43, 571 -585.

AMA Style

Junseo Bae, Kunhee Choi. A new perspective for precision evaluation of large-scale traffic sensor data measurement. Transportation Planning and Technology. 2020; 43 (6):571-585.

Chicago/Turabian Style

Junseo Bae; Kunhee Choi. 2020. "A new perspective for precision evaluation of large-scale traffic sensor data measurement." Transportation Planning and Technology 43, no. 6: 571-585.

Construction management
Published: 02 November 2019 in Journal of Asian Architecture and Building Engineering
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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.

ACS Style

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 Style

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 (6):507-516.

Chicago/Turabian Style

Ji-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.

Articles
Published: 03 September 2019 in Journal of Asian Architecture and Building Engineering
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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.

ACS Style

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 Style

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 (5):472-478.

Chicago/Turabian Style

Ji-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.