Liang zhao at Hebei University of Engineering, China
Liang Zhao is a Ph.D. candidate in Architecture at Southeast University, specializing in Building Information Modeling (BIM), machine learning, generative architectural design, and carbon emissions analysis in buildings. With over five years of experience in BIM development, digital modeling, and lifecycle information systems, he has contributed significantly to research and innovation in architectural heritage preservation and prefabricated building technologies.
Professional Profile:
Scopus
Education Background
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Ph.D. in Architecture, Southeast University, Nanjing, China (Mar 2019 – Present)
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M.S. in HVAC and Gas Engineering, Hebei University of Engineering (Sep 2012 – Jun 2015)
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B.E. in Building Environment and Equipment Engineering, Hebei University of Engineering (Sep 2008 – Jun 2012)
Professional Development
Liang served as a BIM Developer at the China Architecture Design & Research Group (2015–2019), where he contributed to the CBIM comprehensive solution, focusing on program development and system integration. He has also led and participated in major national research initiatives, producing impactful software tools and securing patents in BIM-related technologies.
Research Focus
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Building Information Modeling (BIM)
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Machine Learning in Architecture
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Generative Design and HVAC Systems
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Lifecycle Management for Architectural Heritage
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Carbon Emission and Cost Estimation in Prefabricated Buildings
Author Metrics:
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First inventor on multiple utility model patents
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Holder of several national software copyrights
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Author of an Excellent Dissertation titled Generative Design and Application of HVAC Systems in Residential Buildings Based on BIM and Machine Learning
Awards and Honors:
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Multiple national patents (utility models)
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Recognized software copyrights
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Excellent Dissertation distinction for Ph.D. thesis
Publication Top Notes
1. BIM-Based Analysis and Strategies to Reduce Carbon Emissions of Underground Construction in Public Buildings: A Case on Xi’an Shaanxi, China
Citation:
Han, Y., Wang, Y., Zhao, L., & Wang, H. (2024). BIM-Based Analysis and Strategies to Reduce Carbon Emissions of Underground Construction in Public Buildings: A Case on Xi’an Shaanxi, China. Buildings.
Authors:
Yuheng Han, Yue Wang, Liang Zhao, Haining Wang
Journal:
Buildings
Publication Date:
July 2024
Summary:
This study addresses the high carbon emissions in the underground construction phase of public buildings. It introduces a BIM-based method to assess emissions from various construction stages including raw material extraction, equipment production, and installation. Analyzing 125 real-world cases from Xi’an, China, the study finds a strong link between larger underground space and higher embodied carbon emissions. The research identifies 16 and 19 influencing factors for buildings without and with underground spaces respectively, providing useful insights for early design decisions to reduce emissions.
2. Integrating BIM and Machine Learning to Predict Carbon Emissions under Foundation Materialization Stage: Case Study of China’s 35 Public Buildings
Citation:
Wang, H., Wang, Y., Zhao, L., & Lv, Y. (2024). Integrating BIM and Machine Learning to Predict Carbon Emissions under Foundation Materialization Stage: Case Study of China’s 35 Public Buildings. Frontiers of Architectural Research.
Authors:
Haining Wang, Yue Wang, Liang Zhao, Yihan Lv
Journal:
Frontiers of Architectural Research
Publication Date:
March 2024
Summary:
This paper combines Building Information Modeling (BIM) with machine learning techniques to create a predictive model for carbon emissions during the foundation phase of construction. Using data from 35 public buildings in China, the study applies statistical modeling and AI to accurately forecast carbon output. The findings suggest that this integrative method enhances precision and offers a proactive approach to managing emissions in early construction planning.
3. Carbon Emission Analysis of Precast Concrete Building Construction: A Study on Component Transportation Phase Using Artificial Neural Network
Citation:
Wang, H., Zhao, L., Zhang, H., & Wang, Z. (2023). Carbon Emission Analysis of Precast Concrete Building Construction: A Study on Component Transportation Phase Using Artificial Neural Network. Energy and Buildings.
Authors:
Haining Wang, Liang Zhao, Hong Zhang, Zixiao Wang
Journal:
Energy and Buildings
Publication Date:
December 2023
Summary:
Focusing on the transportation phase of precast concrete construction, this paper applies Artificial Neural Networks (ANNs) to analyze carbon emissions related to component logistics. The model predicts emissions with high accuracy and highlights key variables contributing to high carbon output. The study underscores the need for optimized transportation planning to mitigate environmental impacts during prefabricated building processes.
4. An Analysis of the Spatio-Temporal Behavior of COVID-19 Patients Using Activity Trajectory Data
Citation:
Shen, X., Yuan, H., Jia, W., & Zhao, L. (2023). An Analysis of the Spatio-Temporal Behavior of COVID-19 Patients Using Activity Trajectory Data. Heliyon.
Authors:
Xiumei Shen, Hao Yuan, Wenzhao Jia, Liang Zhao
Journal:
Heliyon
Publication Date:
October 2023
Summary:
This study examines the spatio-temporal movement patterns of COVID-19 patients in Nanjing and Yangzhou, China. Using activity trajectory data and complex network theory, the authors identify critical transmission nodes such as residential areas and vegetable markets. Findings reveal differences in movement behavior based on outbreak location (central vs. suburban), and provide actionable insights for improving pandemic containment strategies.
5. Building a Satisfactory Indoor Environment for Healthcare Facility Occupants: A Literature Review
Citation:
Shen, X., Zhang, H., Li, Y., & Jia, W. (2022). Building a Satisfactory Indoor Environment for Healthcare Facility Occupants: A Literature Review. Building and Environment.
Authors:
Xiumei Shen, Hong Zhang, Ying Li, Wenzhao Jia
Journal:
Building and Environment
Publication Date:
November 2022
Summary:
This review investigates how indoor environmental quality (IEQ) influences the satisfaction of healthcare facility users. Drawing from 98 studies (2001–2022), the paper focuses on visual, acoustic, thermal environments, and indoor air quality. It identifies low-cost interventions and the importance of user control over their environment. A conceptual framework is proposed to help designers and administrators enhance occupant satisfaction and health outcomes in healthcare settings.
Conclusion
Liang Zhao exemplifies the attributes of a forward-thinking, innovative researcher whose work not only pushes academic boundaries but also solves real-world problems. His multidisciplinary approach—merging BIM, AI, and sustainability—makes him a strong contender for the Best Researcher Award.
Final Recommendation:
✅ Highly suitable for the award. Recognizing Liang Zhao would honor an emerging thought leader in sustainable architecture and inspire further research in this vital area.