Junseo Bae

Assistant Professor

Sejong University

South Korea

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Junseo Bae is an Assistant Professor at Korea University - Sejong Campus in South Korea. His research focuses on the application of machine learning and deep learning techniques in various domains, including accident risk assessment, thermal infrared measurements, and natural disaster risk mitigation. His recent articles reflect a strong emphasis on predictive analytics, construction safety, and loss assessment related to natural hazards.

Junseo Bae

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Articles (18)

Machine Learning Approach for Ex-Post Evaluation of Road Traffic Collision Severity Trends

Thermal‐Infrared Measurements of the North‐East Tower at Ardrossan Castle

Deploying predictive analytics to reduce accident risks of construction migrant workers on sites: a case of South Korea

A deep-learning approach to leveraging natural hazard indicators for improved safety on construction sites

Supervised learning to covering cost risk through post-construction evaluation of transportation projects by project delivery methods

A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

Building loss assessment using deep learning algorithm from typhoon Rusa

A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings

A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula

Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis

Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

OPTID: Optimal Incentives and Disincentives

A deep learning algorithm-driven approach to predicting repair costs associated with natural disaster indicators: The case of accommodation facilities

Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

A supervised learning approach to calibrating annual average daily traffic against highway roadworks: the impact of demographic and weather conditions

A land-use clustering approach to capturing the level-of-service of large urban corridors: A case study in downtown Los Angeles

Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities

Collaborators (5)

Young Hoon Kwak

Ji-Myong Kim

Kiyoung Son

Simon Oh

Christopher Brooke

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