Dr. JOONSOO KIM | Digital Transformation | Best Researcher Award
Postdoctoral Researcher, Korea Institute of Construction Technology (KICT), South Korea.
Dr. Joon-Soo Kim is an innovative and skilled researcher in Construction Management, with a strong command of advanced data analytics, machine learning, and environmental sustainability. His academic and research work uniquely integrates engineering with cutting-edge technologies such as text mining and big data to enhance construction safety and efficiency. Currently serving as a Postdoctoral Researcher at the Korea Institute of Civil Engineering and Building Technology (KICT), Dr. Kim is highly regarded for developing reliable decision-making models tailored to complex engineering challenges. His expertise spans from eco-friendly engineering solutions to smart waste management systems, making him a vital contributor to sustainable civil engineering research. 
Publication Profile
Education Background
Dr. Kim earned his Ph.D. in Construction Environment and Energy Engineering from Kyungpook National University, where his dissertation explored road construction environmental load and cost estimation using machine learning. He also holds a Master’s in Construction Systems Engineering with a specialization in Geotechnical and Road Engineering, and a Bachelor’s degree in Civil Engineering—all from the same university. His academic path reflects a strong commitment to applying data-driven insights to real-world infrastructure problems. 
Professional Experience
Dr. Kim is currently a Postdoctoral Researcher at KICT, contributing to advancements in civil infrastructure and environmental solutions. Prior to this, he worked at the Intelligent Construction Automation Research Center, Kyungpook National University, for over three years. He also shared his expertise as a lecturer at Daegu University, teaching courses such as Basic Statistics and Construction Management. His combined academic and field experience empowers him to lead high-impact research in civil engineering. 
Awards and Honors
Dr. Kim holds registered intellectual property rights, including a software-based Construction Waste Information Management (CWIM) system using QR codes (2023), and a patented Eco-Friendly Value Engineering Decision Analysis System (Patent No. 10-1745567, registered in 2017). These innovations underscore his commitment to enhancing efficiency and sustainability in construction engineering through smart technologies. 
Research Focus
Dr. Kim’s primary research areas include construction safety, environmental load management, and project efficiency, with a strong focus on big data, machine learning, and geospatial analysis. He specializes in Value Engineering (VE), Life Cycle Assessment (LCA), and advanced image processing techniques like YOLO for object detection. His multidisciplinary approach supports disaster prevention and promotes green building practices. 
Conclusion
Combining strong academic foundations with hands-on innovation, Dr. Joon-Soo Kim continues to make significant strides in the civil and construction engineering fields. His work not only enhances safety and environmental responsibility but also sets a benchmark in leveraging AI-driven methodologies for engineering problem-solving. 
Top Publications with
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Image Processing and QR Code Application Method for Construction Safety Management
Kim, J.-S., Yi, C.-Y., & Park, Y.-J., 2021, Applied Sciences
Cited by: 18 articles (as per Google Scholar) -
Impact Evaluation of Water Footprint on Stages of Drainage Works
Chen Di, Kim, J.-S., Batagalle V., & Kim, B.-S., 2020, Journal of the Korean Society of Civil Engineers Cited by: 6 articles -
Characteristics Analysis of Seasonal Construction Site Fall Accident using Text Mining
Kim, J.-S., & Kim, B.-S., 2019, Journal of the Korea Institute of Construction Engineering and Management Cited by: 11 articles -
Analysis of the Environmental Load Impact Factors for IPC Girder Bridge Using PCA
Kim, J.-S., Jeon, J.-G., & Kim, B.-S., 2018, Journal of the Korea Institute of Construction Engineering and Management Cited by: 9 articles -
Analysis of Fire-Accident Factors Using Big-Data in Construction Areas
Kim, J.-S., & Kim, B.-S., 2017, KSCE Journal of Civil Engineering Cited by: 34 articles -
Eco-Friendly Design Evaluation Model Using PEI for Construction Facilities
Kim, J.-S., & Kim, B.-S., 2017, Journal of the Korean Society of Civil Engineers Cited by: 10 articles -
Definition of Environmental Cost and Eco-VE Model for Construction Facility
Kim, M.-J., Kim, J.-S., & Kim, B.-S., 2016, Journal of the Korean Society of Civil Engineers Cited by: 7 articles -
A Proposal of NIA Model for Eco VE Decision of Construction Facilities
Kim, J.-S., & Kim, B.-S., 2015, Journal of the Korean Society of Civil Engineers Cited by: 12 articles
. He is currently serving as an Assistant Professor in the Department of MCA at G. L. Bajaj College of Technology and Management, Greater Noida, India 
. With a strong academic background and years of teaching experience, Dr. Alam is dedicated to advancing knowledge in secured workflow allocation and innovative computing methodologies. His research contributions are well-recognized, with publications in reputed journals, including IEEE, Springer, Elsevier, and Wiley 
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. Currently, he holds the position of Assistant Professor at G. L. Bajaj College of Technology and Management, Greater Noida. His dedication to mentoring students and contributing to research has made him a respected figure in academia. His expertise extends to cutting-edge domains such as heuristic and meta-heuristic approaches in secured workflow allocation 
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. His contributions to machine learning applications and 
. He explores innovative approaches to software defect prediction, cybersecurity in IoT, and machine learning-driven optimization techniques. His research integrates heuristic, meta-heuristic, and reinforcement learning methods to address challenges in secure computing environments 
Conclusion
. With a strong passion for advancing knowledge and technology, Dr. Alam remains a prominent figure in the global research community 
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