Ation Lab
The suffix '-ation' embodies the process or action leading to generating outcomes - which is a fundamental aspect of research in engineering
The suffix '-ation' embodies the process or action leading to generating outcomes - which is a fundamental aspect of research in engineering
Research Interests
Building Information Modeling
Digitalization of the built environment (buildings & industrial facilities)
Automation in construction
Electrification
De-carbonization
Circular Economy
Computer Vision
Artificial Intelligence
Reality Capture (3D Laser Scanning)
Lab members (current)
Assistant Professor
Lab Director (2023 - Present) <jongwon.ma@yonsei.ac.kr>
Previous affiliation: Assistant Professor at Concordia University (2023 - 2025) / PhD in Civil Engineering at the University of Texas at Austin (2022) / MS & BS in Civil Engineering at Yonsei University (2018 & 2016)
My research interests revolve around the convergence of science, technology, and the built environment, with a particular focus on the digitalization of workflows leveraging artificial intelligence and computer vision. His specialty is in as-built Scan-to-Building Information Modeling, an automated approach for the digital transformation of existing infrastructure and building systems that employs reality capture technologies to generate a 3D digital replica of the physical world. His past research efforts include streamlining construction workflows by expanding the scope of advanced work packaging to encompass commissioning and startup processes, as well as enhancing local community resilience through a combination of qualitative and quantitative approaches. In addition to my studies, I find joy in various outdoor activities such as tennis, working out at the gym, and exploring new delicious restaurants.
PhD Program
(2023 - Present
<moaaz.elkabalawy@mail.concordia.ca>
Previous affiliation: MS in Civil Engineering at Concordia University / BS in Civil and Environmental Engineering at University of Sharjah
As a Ph.D. student, my primary focus encompasses the cutting-edge domain of modular offsite construction digitalization through computer vision technologies and exploring the broader spectrum of the 4.0 industrial revolution in digital construction. This multifaceted research interest is aimed at revolutionizing the construction industry by harnessing digital transformation tools to improve efficiency, accuracy, and sustainability. Beyond the confines of my research, I am a big fan of soccer reading, and hiking. While soccer supports my physical health and allows me to socialize with friends, reading opens the doors to diverse worlds of knowledge, culture, and imagination. As for hiking, it allows me to reconnect with nature to find peace away from the technological and digital pursuits of my professional life.
MS & PhD Program
(2024 - Present)
<deokyeong.kim@mail.concordia.ca>
Deokyeong Kim (Co-advised by Dr. Leila Kosseim)
Previous affiliation: BS in Computer Science at Concordia University
I have a background in computer science and a keen interest in Artificial Intelligence, machine learning, and deep learning. Currently, my primary focus lies in conducting Natual Language Processing research related to crane safety within the civil engineering domain. Through this research, I aim to contribute to solving and improving safety issues in industrial settings. Outside of my professional endeavors, I enjoy hobbies such as playing the guitar, baking, and golfing, which help me maintain a balance in life and engage in creative activities.
PhD Program
(2024 - Present)
<c_mdraki@live.concordia.ca>
Md Rakibul Islam Chowdhury (Co-advised by Sanghyeok Han)
Previous affiliation: M.Eng (Thesis) in Construction Engineering and Infrastructure Management at Asian Institute of Technology, Thailand / BS in Building Engineering and Construction Management at Khulna University of Engineering & Technology, Khulna, Bangladesh
I am currently immersed in Ph.D. research that revolves around the innovative application of Computer Vision technologies in the field of Modular and Offsite Construction, with a particular focus on Point Cloud-based segmentation. My work is dedicated to advancing digital transformation in construction, harnessing the power of artificial intelligence and 3D modeling to automate and optimize the design, assembly, and quality assurance of offsite modular structures. This research is aligned with the broader goals of Industry 4.0, aiming to push the boundaries of digitization by incorporating cutting-edge methods to increase efficiency, reduce errors, and enhance sustainability.
Beyond my academic commitments, I have a deep love for cricket. Hiking is another passion of mine that allows me to step away from the fast-paced world of technology and find tranquility in nature. It serves as a grounding force in my life, helping me maintain a balanced perspective between my career and personal well-being.
MASc Program
(2025 - Present)
<2025321216@yonsei.ac.kr>
Umaira Husna Binti Abdul Rahman
Previous affiliation: Bachelor of Quantity Surveying in Architecture & Environmental Design at Building Engineering and Construction Management at International Islamic University Malaysia
Currently pursuing my Master’s studies in Civil and Environmental Engineering, I am deeply motivated by the potential of digital technologies, particularly Building Information Modeling (BIM) to advance the future of construction management. My academic and research interests focus on how digital tools like BIM can be applied to improve project performance by enhancing cost control, time efficiency and quality management across the construction lifecycle. Through this, I aspire to contribute to the transformation of construction management practices, fostering more efficient, reliable, and value-driven outcomes. Outside of my academic pursuits, I enjoy running which helps me maintain mental clarity and a healthy, balanced lifestyle.
Undergraduate Intern
(2025 - Present)
<smile6569@yonsei.ac.kr>
Jeongkyu Lee
Current affiliation: BS (expected in 2026) in Civil and Environmental Engineering at Yonsei University
I am currently majoring in Civil and Environmental Engineering and have a strong interest in Artificial Intelligence. My primary focus lies in exploring BIM (Building Information Modeling) and Digital Twin technologies, aiming to integrate AI techniques to enhance construction management, safety, and efficiency within the civil engineering domain. Through this research and study, I aim to contribute to the development of innovative solutions for complex infrastructure challenges.
Undergraduate Intern
(2025 - Present)
<wjdgkqls@yonsei.ac.kr>
Habin Jung
Current affiliation: BS (expected in 2026) in Civil and Environmental Engineering at Yonsei University
I am an undergraduate student in Civil and Environmental Engineering with academic interests in BIM, 3D modeling, and artificial intelligence in civil engineering. My focus is on exploring and expanding digitalization and innovation in construction by integrating artificial intelligence into BIM, with the goal of advancing efficiency, safety, and sustainability in future infrastructure.
Outside my academic pursuits, I enjoy fitness training to maintain the physical strength needed for research and play the violin to preserve artistic sensibility and creativity in my life.
Lab members (Past)
Jun-Hyoung Park
Undergraduate Intern
(2024 -2025)
<jun-hyoung.park@mail.concordia.ca>
Xi Luo
MASc
(2025)
<xi.luo@mail.concordia.ca>
JOIN OUR LAB!!
Ation lab is seeking new talented team members for both MASc & PhD positions!
Please access this link for your application!
Past Research Projects
PI, University Student Research Award
Funding Agency: Natural Sciences and Engineering Research Council of Canada
Period: 2024/05 - 2024/08
Students: Jun-Hyoung Park
PI, RT423 - Enhancing Crane Safety: Mining Insights and Patterns for Predictive, Preventive, and Proactive Analysis
Funding Agency: Construction Industry Institute
Period: 2024/02 - 2024/08
Students: Deokyeong Kim
Co-PI, Electrified, Resilient, and Decarbonized Communities Grant - Eco-friendly Smart Construction Systems for Decarbonization of Construction Industry
Funding Agency: Canada First Research Excellence
Period: 2024/03 - 2026/03
Students: TBD (Hiring)
Co-PI, Electrified, Resilient, and Decarbonized Communities Grant - Digital Twins for Smart Decarbonization of the Built Environment Meeting Circular Economy Criteria
Funding Agency: Canada First Research Excellence
Period: 2024/03 - 2026/03
Students: TBD (Hiring)
PI, Discovery Grant - Digitalization of Industrial Facilities using Scan-to-BIM
Funding Agency: Natural Sciences and Engineering Research Council of Canada
Period: 2023/04 - 2028/03
Students: Moaaz Elkabalawy & Xi Luo
Publications
Ma, J.W.*; Jung, J.; Leite, F. (2024) “Deep Learning-based Scan-to-BIM Automation and Object Scope Expansion using a Low-Cost 3D Scan Data”, Journal of Computing in Civil Engineering
Ma, J.W.*; Leite, F.; Lieberknecht, K.; Stephens, K.; Bixler, R.P. (2024) “Using Q-methodology to Discover Disaster Resilience Perspectives from Local Residents”, International Journal of Disaster Risk Reduction
Lee, Y.; Ma, J.W.; Leite, F. (2023) “A Parametric Approach Towards Semi-Automated 3d As-Built Modeling", Journal of Information Technology in Construction, https://doi.org/10.36680/j.itcon.2023.041
Park, S.; Ma, J.W.* (2023) “Voxel-based Structural Monitoring Model for Building Structures Using Terrestrial Laser Scanning”, Journal of Building Engineering, https://doi.org/10.1016/j.jobe.2023.108151
Ma, J.W.*; Czerniawski, T.; Leite, F. (2022) Chapter: Automated Scan-to-BIM, Research Companion on Building Information Modeling (Wiley), https://dx.doi.org/10.4337/9781839105524
Ma, J.W.*; Leite, F. (2022) “Performance boosting of conventional deep learning-based semantic segmentation leveraging unsupervised clustering”, Automation in Construction, https://doi.org/10.1016/j.autcon.2022.104167
O’Connor, J. T.; Leite, F.; Ma, J.W.* (2022) “Expanding the Advanced Work Packaging Scope to include Commissioning and Startup for Industry Projects”, Construction Innovation, https://doi.org/10.1108/CI-09-2021-0173
Ma, J.W.*; Czerniawski, T.; Leite, F. (2021) “An Application of Metadata-based Image Retrieval System for Facility Management”, Advanced Engineering Informatics, https://doi.org/10.1016/j.aei.2021.101417
Han, B.; Ma, J.W.; Leite, F. (2021) “A Framework for Automatically Identifying Occluded Objects in 3D Models: Towards Comprehensive Construction Design Review in Virtual Reality”, Advanced Engineering Informatics, https://doi.org/10.1016/j.aei.2021.101398
Ju, S.; Lim, H.; Ma, J.W.; Kim, S.; Lee, K.; Zhao, S.; Heo, J. (2021) “Optimal County-level Crop Yield Prediction Using MODIS-based Vegetation Indices and Weather Data: A Comparative Study on Machine Learning Models”, Agricultural and Forest Meteorology, https://doi.org/10.1016/j.agrformet.2021.108530
Czerniawski, T.; Ma, J.W.; Leite, F. (2021) “Automated Building Change Detection with Amodal Completion of Point Clouds”, Automation in Construction, https://doi.org/10.1016/j.autcon.2021.103568
Ma, J.W.*; Czerniawski, T.; Leite, F. (2020) “Semantic Segmentation of Point Clouds of Building Interiors with Deep Learning: Augmenting Training Datasets with Synthetic BIM-based Point Clouds”, Automation in Construction, https://doi.org/10.1016/j.autcon.2020.103144
Ma, J.W.; Nguyen, C.H.; Lee, K.; Heo, J. (2019) “Regional-Scale Rice Yield Estimation Using Stacked Auto-encoder with Climatic and MODIS data: a case study of South Korea”, International Journal of Remote Sensing, https://doi.org/10.1080/01431161.2018.1488291
Ma, J.W.; Lee, K.; Choi. K.; Heo, J. (2017) “Rice Yield Estimation of South Korea from Year 2003-2016 Using Stacked Sparse AutoEncoder”, Korean Journal of Remote Sensing, https://doi.org/10.7780/kjrs.2017.33.5.2.3
Nguyen, M.H.; Ma, J.W.; Lee, K.; Heo, J. (2017) “The Design of Web-based Crop Information System Using Open-Source Framework and Remotely Sensed Data”, Korean Journal of Remote Sensing, https://doi.org/10.7780/kjrs.2017.33.5.2.14
Nguyen, M.H.; Ju, S.; Ma, J.W.; Heo, J. (2017) “A Benchmark Test of Spatial Big Data Processing Tools and a MapReduce Application”, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, https://doi.org/10.7848/ksgpc.2017.35.5.405
Ma, J.W.; Nguyen, C.H.; Lee, K.; Heo, J. (2016) “Convolutional Neural Networks for Rice Yield Estimation Using MODIS and Weather Data: A Case Study for South Korea”, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, https://doi.org/10.7848/ksgpc.2016.34.5.525