Buildings, Energy and Environment Research Group
 

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Zigeng Fang

Assistant Professor in Civil Engineering and Management, Faculty of Engineering

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Biography

Dr Zigeng Fang is an Assistant Professor in Civil Engineering and Management in the Department of Civil Engineering at the University of Nottingham. Despite his engagement in teaching at both undergraduate and postgraduate levels, he mainly focuses on teaching the postgraduate level course - Civil Engineering and Management MSc.

He joined the university in June 2023. Before that, he was a research fellow in Digital Building Twins for the COGITO project under the Horizon Europe Programme at the Institute for Environmental Design and Engineering, University College London. He obtained his BEng in Building Service Engineering from the University of Hong Kong, MSc in Facility and Environment Management, MRes in Architectural Computation, and PhD in Construction Project Management from University College London. Along with his research experience, He previously shouldered the data analyst/scientist role in the Strategic Asset Management Team of the Vercity Social Infrastructure and the Asseticom.

Research Summary

He primarily concentrates on three areas of research: Smart, Shareable, and Sustainable. For the smart aspect, he aims to materialise the digitalisation benefits of the construction industry by using… read more

Recent Publications

  • LU, QIUCHEN, CHEN, LONG, XIE, XIANG, FANG, ZIGENG, YE, ZHEN and PITT, MICHAEL, 2023. Framing blockchain-integrated digital twins for emergent healthcare management at local and city levels: a proof of concept In: Proceedings of the Institution of Civil Engineers-Engineering Sustainability. 1-14
  • FANG, ZIGENG, LU, QIUCHEN, CHEN, LONG, MENG, JIAYIN, YE, ZHEN and PITT, MICHAEL, 2023. Creating an Open Data City for Healthcare: A Critical Review of Data Management Strategy and Development in China Journal of Management in Engineering. 39(2), 03122004
  • FANG, ZIGENG, YAN, JIAYI, LU, QIUCHEN, CHEN, LONG, YANG, PU, TANG, JUNQING, JIANG, FENG, BROYD, TIM and HONG, JINGKE, 2023. A systematic literature review of carbon footprint decision-making approaches for infrastructure and building projects Applied Energy. 335, 120768
  • FANG, ZIGENG, LU, QIUCHEN, XIE, XIANG, PARLIKAD, AJITH KUMAR, SCHOOLING, JENNIFER and PITT, MICHAEL, 2022. Data-model integration layer. In: Digital Twins in the Built Environment: Fundamentals, principles and applications ICE Publishing. 161-204

Current Research

He primarily concentrates on three areas of research: Smart, Shareable, and Sustainable. For the smart aspect, he aims to materialise the digitalisation benefits of the construction industry by using various tools and technologies (e.g., Machine Learning, Digital Twins, Building Information Modeling, etc.) of digital construction and asset management for fulfilling the requirement of industry 4.0. For the shareable aspect, he aims to connect the isolated "data islands" of different construction projects by solving the interoperability, standardisation, and quality control issues to promote knowledge transfer and portfolio-based built asset information management. For the sustainable aspect, he aims to develop a digitalised continuous way of managing the carbon footprint of buildings and infrastructure.

  • LU, QIUCHEN, CHEN, LONG, XIE, XIANG, FANG, ZIGENG, YE, ZHEN and PITT, MICHAEL, 2023. Framing blockchain-integrated digital twins for emergent healthcare management at local and city levels: a proof of concept In: Proceedings of the Institution of Civil Engineers-Engineering Sustainability. 1-14
  • FANG, ZIGENG, LU, QIUCHEN, CHEN, LONG, MENG, JIAYIN, YE, ZHEN and PITT, MICHAEL, 2023. Creating an Open Data City for Healthcare: A Critical Review of Data Management Strategy and Development in China Journal of Management in Engineering. 39(2), 03122004
  • FANG, ZIGENG, YAN, JIAYI, LU, QIUCHEN, CHEN, LONG, YANG, PU, TANG, JUNQING, JIANG, FENG, BROYD, TIM and HONG, JINGKE, 2023. A systematic literature review of carbon footprint decision-making approaches for infrastructure and building projects Applied Energy. 335, 120768
  • FANG, ZIGENG, LU, QIUCHEN, XIE, XIANG, PARLIKAD, AJITH KUMAR, SCHOOLING, JENNIFER and PITT, MICHAEL, 2022. Data-model integration layer. In: Digital Twins in the Built Environment: Fundamentals, principles and applications ICE Publishing. 161-204
  • FANG, ZIGENG, LU, QIUCHEN, XIE, XIANG, PARLIKAD, AJITH KUMAR, SCHOOLING, JENNIFER and PITT, MICHAEL, 2022. Definitions and principles of digital twins. In: Digital Twins in the Built Environment: Fundamentals, principles and applications ICE Publishing. 5-27
  • XIE, XIANG, FANG, ZIGENG, CHEN, LONG, LU, QIUCHEN, TAN, TAN, YE, ZHEN and PITT, MICHAEL, 2022. Facilitating patient-centric thinking in hospital facility management: A case of pharmaceutical inventory Buildings. 12(7), 888
  • FANG, ZIGENG, LIU, YAN, LU, QIUCHEN, PITT, MICHAEL, HANNA, SEAN and TIAN, ZHICHAO, 2022. BIM-integrated portfolio-based strategic asset data quality management Automation in Construction. 134, 104070
  • FANG, ZIGENG, TAN, TAN, YAN, JIAYI, LU, QIUCHEN, PITT, MICHAEL and HANNA, SEAN, 2022. Automated portfolio-based strategic asset management based on deep neural image classification Automation in Construction. 142, 104481
  • TAN, TAN, FANG, ZIGENG, ZHENG, YUANWEI and YANG, YUFENG, 2021. Health Building Information Modeling (HBIM)-Based Facility Management: A Conceptual Framework In: International Symposium on Advancement of Construction Management and Real Estate. 136-146
  • LU, QIUCHEN, YE, ZHEN, FANG, ZIGENG, MENG, JIAYIN, PITT, MICHAEL, LIN, JINYI, XIE, XIANG and CHEN, LONG, 2021. Creating an inter-hospital resilient network for pandemic response based on blockchain and dynamic digital twins In: 2021 Winter Simulation Conference (WSC). 1-12
  • FANG, ZIGENG, PITT, MICHAEL and HANNA, SEAN, 2019. Machine learning in facilities & asset management In: Proceedings of the 25th annual Pacific rim real estate society (PRRES) conference.
  • TIAN, ZHICHAO, SI, BINGHUI, SHI, XING and FANG, ZIGENG, 2019. An application of Bayesian Network approach for selecting energy efficient HVAC systems Journal of Building Engineering. 25, 100796

Buildings, Energy and Environment Research Groups

Faculty of Engineering
The University of Nottingham
University Park
Nottingham, NG7 2RD


telephone: +44 (0) 115 74 86257
email: BEE@nottingham.ac.uk