Byung Kwan Oh, Ph.D., is an accomplished Assistant Professor in the Department of Architecture and Architectural Engineering at Yonsei University. With a rich academic background in structural engineering, Byung Kwan focuses on innovative research areas such as artificial intelligence-based safety evaluation for buildings and multi-objective structural design. His expertise lies in structural health monitoring, seismic retrofitting, and sustainable design methodologies. Byung Kwan’s dedication to advancing knowledge in these fields is evident through his extensive publication record and active involvement in academia.

Author Metrics:

Byung Kwan Oh’s impact as a researcher is evidenced by his author metrics, including citation counts, h-index, and other measures of scholarly influence. His research publications have garnered attention from peers and experts in the field, demonstrating the significance and relevance of his work in advancing structural engineering practices.

Orcid Profile

Google Scholar Profile

Scopus Profile

Byung Kwan Oh is affiliated with Yonsei University in Seoul, South Korea. He has a Scopus Author Identifier with the ID 56134318500. Byung Kwan Oh has an ORCID profile with the ID 0000-0002-7422-2947. According to Scopus, he has 1,222 citations from 975 documents. Byung Kwan Oh has authored 53 documents and has an h-index of 22.

Education:

Byung Kwan Oh pursued his educational journey at Yonsei University in South Korea. He earned his Bachelor of Science in Architectural Engineering followed by a Master of Science and a Ph.D. in Structural Engineering. His doctoral thesis focused on modal influence-based system identification and sustainable structural health monitoring for buildings, showcasing his commitment to advancing structural engineering practices.

Research Focus:

Byung Kwan Oh’s research interests revolve around two main areas: artificial intelligence-based safety evaluation for buildings and multi-objective structural design. He utilizes cutting-edge techniques such as deep learning for damage detection, structural dynamics analysis, and optimization algorithms for seismic retrofitting and sustainable design. His work aims to enhance the safety, efficiency, and sustainability of building structures through innovative methodologies and technologies.

Professional Journey:

Byung Kwan Oh has built a diverse professional journey encompassing academic, research, and practical experiences. From his early days as an Associate Engineer at Posco E&C to his tenure as a Visiting Post-Doctoral Research Associate at Princeton University, he has gained valuable insights into various aspects of structural engineering. He has held positions as a Research Professor, Post-Doctoral Fellow, Lecturer, and Assistant Professor at esteemed institutions in South Korea and the United States, contributing significantly to both education and research in his field.

Honors & Awards:

Throughout his career, Byung Kwan Oh has been recognized for his outstanding contributions to structural engineering. His notable achievements include awards for his research publications, exemplary teaching performance, and impactful contributions to the field. These honors underscore his dedication to excellence and his significant impact on the academic and professional community.

Publications Top Noted & Contributions:

Byung Kwan Oh’s contributions to the field of structural engineering are reflected in his extensive publication record. He has authored numerous research papers, book chapters, and conference presentations that have advanced knowledge in structural health monitoring, seismic retrofitting, and sustainable design. His work has been widely cited and has made significant contributions to the academic discourse in his field.

“Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings” (2017, Applied Soft Computing):

• Authors: BK Oh, KJ Kim, Y Kim, HS Park, H Adeli
• Presents a sustainable strain sensing model for monitoring the structural health of high-rise buildings.
• Utilizes evolutionary learning techniques to develop an efficient and sustainable approach to strain sensing.

“Convolutional neural network‐based wind‐induced response estimation model for tall buildings” (2019, Computer-Aided Civil and Infrastructure Engineering):

• Authors: BK Oh, B Glisic, Y Kim, HS Park
• Introduces a model for estimating wind-induced responses in tall buildings.
• Utilizes convolutional neural networks (CNNs) to improve the accuracy and efficiency of response predictions.

“Vision-based system identification technique for building structures using a motion capture system” (2015, Journal of Sound and Vibration):

• Authors: BK Oh, JW Hwang, Y Kim, T Cho, HS Park
• Discusses a vision-based technique for identifying system properties of building structures.
• Uses a motion capture system to gather visual data and analyze it for structural parameter identification.

“Real-time structural health monitoring of a supertall building under construction based on visual modal identification strategy” (2018, Automation in Construction):

• Authors: HS Park, BK Oh
• Describes a real-time structural health monitoring system for a supertall building under construction.
• Utilizes a visual modal identification strategy to assess the building’s health and integrity during construction.

“Convolutional neural network–based data recovery method for structural health monitoring” (Structural Health Monitoring):

• Authors: BK Oh, B Glisic, Y Kim, HS Park
• Introduces a method for recovering missing or corrupted data in structural health monitoring systems.
• Utilizes convolutional neural networks (CNNs) to reconstruct missing data, enhancing monitoring reliability.

Research Timeline:

Byung Kwan Oh’s research journey spans over a decade, beginning with his doctoral studies at Yonsei University and continuing through his various academic and research positions. His timeline reflects a progression from foundational research in structural dynamics and health monitoring to more advanced studies in artificial intelligence-based safety evaluation and multi-objective structural design. Each phase of his research journey has contributed to the evolution of his expertise and the advancement of knowledge in his field.