Assoc. Prof. Dr. Siyuan Song, Arizona State University, United States
Dr. Siyuan Song is an Associate Professor at Arizona State University’s Del E. Webb School of Construction, within the School of Sustainable Engineering and the Built Environment. He specializes in construction safety, AI in construction, and workforce development. Dr. Song earned his Ph.D. in Civil Engineering from the University of Alabama, where he focused on construction equipment productivity. He has a strong background in construction engineering and management, which he combines with cutting-edge research in construction automation and robotics. He is dedicated to advancing safety in the construction industry through innovative training programs and workforce development initiatives. Dr. Song has contributed extensively to the field with a focus on enhancing safety protocols in high-risk environments, such as construction sites and surface mining.
Professional Profile
Google Scholar
Suitability For the Best Researcher Award
Siyuan Song, Ph.D., is an exceptional candidate for the Research for Best Researcher Award due to his extensive contributions to construction safety, workforce development, and AI in construction. As an Associate Professor at Arizona State University, Dr. Song has built a distinguished academic and professional portfolio. His research focuses on addressing critical issues in construction, such as workplace safety, health training, automation, and robotics. His work aligns with key global challenges, particularly in improving safety and health conditions for construction workers, an area in which he has secured significant research funding.
Dr. Song’s professional achievements include receiving multiple prestigious awards, such as the Best Division Paper Award from the American Society for Engineering Education (ASEE) in 2023, and the Outstanding Contribution to Workplace Industry Training Award at the Immersive Learning Research Network (iLRN) Annual Conference in 2023. His dedication to advancing construction safety is evident in his leadership of numerous research projects funded by organizations like the Department of Labor (OSHA and MSHA), focusing on heat-related illness prevention, hazard awareness, and worker safety.
Education
Dr. Song completed his Ph.D. in Civil Engineering from the University of Alabama in 2017, where his dissertation focused on “Construction Equipment Travel Path Visualization and Productivity Evaluation.” He also holds a Master of Science in Civil Engineering, with a thesis on “Location-Based Tracking of Construction Equipment for Automated Cycle-Time Analysis.” His undergraduate degree, a Bachelor of Science in Construction Engineering and Management, was awarded by Suzhou University of Science and Technology in 2014. His academic path has been defined by his commitment to developing innovative solutions to enhance safety and productivity in the construction industry.
Professional Experience
Dr. Song has extensive teaching and research experience in the field of construction engineering. He currently serves as an Associate Professor at Arizona State University, where he focuses on AI-driven solutions in construction safety. Prior to this, Dr. Song was an Assistant Professor at the University of Alabama and the University of Southern Mississippi. His career has been marked by a strong focus on workforce safety, training, and the use of technology to address challenges in the construction sector. He has also contributed significantly to research grants related to occupational safety and health.
Awards and Recognition
Dr. Song has received numerous awards for his contributions to construction safety and engineering education. Notable honors include the 2023 Best Division Paper Award from the American Society for Engineering Education (ASEE) Annual Conference, and the 2023 University of Alabama 18 Under 31 Young Alumni Award. He also earned the 2022 ASCE ExCEEd Teaching Fellow Award for Excellence in Civil Engineering Education. Dr. Song’s early academic achievements were recognized through several awards at Suzhou University of Science and Technology, including the Outstanding Student Awards and Outstanding Student Leader awards, reinforcing his leadership and excellence in the field of engineering.
Dr. Song’s research is focused on construction safety and the integration of AI and robotics into the industry. He has expertise in workforce development, workplace safety training, and the automation of construction processes. His research methods often combine traditional construction engineering approaches with emerging technologies like AI, data analytics, and robotics. Dr. Song is passionate about enhancing safety standards on construction sites and has developed training programs aimed at preventing heat-related illnesses and improving hazard awareness for workers in high-risk environments.
Publication Top Notes
- Improving tolerance control on modular construction project with 3D laser scanning and BIM: A case study of removable floodwall project
- Authors: H Li, C Zhang, S Song, S Demirkesen, R Chang
- Citation: Applied Sciences 10 (23), 8680
- Year: 2020
- Construction site path planning optimization through BIM
- Authors: S Song, E Marks
- Citation: ASCE International Conference on Computing in Civil Engineering 2019, 369-376
- Year: 2019
- Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects
- Authors: MA Alzarrad, GP Moynihan, MT Hatamleh, S Song
- Citation: Advances in Civil Engineering 2019 (1), 7852301
- Year: 2019
- Impact variables of dump truck cycle time for heavy excavation construction projects
- Authors: S Song, E Marks, N Pradhananga
- Citation: Journal of Construction Engineering and Project Management 7 (2), 11-18
- Year: 2017
- A study on assessing the awareness of heat-related illnesses in the construction industry
- Authors: S Song, F Zhang
- Citation: Construction Research Congress 2022, 431-440
- Year: 2022
- Industrial safety management using innovative and proactive strategies
- Authors: S Song, I Awolusi
- Citation: Concepts, Applications and Emerging Opportunities in Industrial Engineering
- Year: 2020
- Work-related fatalities analysis through energy source recognition
- Authors: S Song, I Awolusi, Z Jiang
- Citation: Construction Research Congress 2020, 279-288
- Year: 2020
- A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines
- Authors: B Sherafat, A Rashidi, S Song
- Citation: Construction Research Congress 2020, 886-895
- Year: 2020
- Steel manufacturing incident analysis and prediction
- Authors: S Song, Q Lyu, E Marks, A Hainen
- Citation: Journal of Safety, Health and Environmental Research 14 (1), 331-336
- Year: 2018
- Impact of discretionary safety funding on construction safety
- Authors: S Song, I Awolusi, E Marks
- Citation: Journal of Safety Health and Environmental Research 13 (2), 378-384
- Year: 2017