World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
11863
World Ranking
2221
National Ranking
448

Overview

Lieyun Ding is affiliated with Huazhong University of Science and Technology in China, focusing primarily on engineering research. Their work is particularly concentrated in several key scientific fields and subfields including civil and structural engineering, building and construction, radiological and ultrasound technology, astronomy and astrophysics, and aerospace engineering.

The main topics covered in their research encompass occupational health and safety research, infrastructure maintenance and monitoring, planetary science and exploration, building information modeling (BIM) and construction integration, risk and safety analysis, blockchain technology applications and security, and geotechnical engineering and analysis.

Lieyun Ding has contributed to numerous publications in a variety of scientific venues, with frequent contributions to:

  • Automation in Construction
  • Engineering
  • Developments in the Built Environment
  • Frontiers of Engineering Management
  • Advanced Engineering Informatics

The researcher has coauthored multiple papers with several frequent collaborators, including:

  • Cheng Zhou
  • Ying Zhou
  • Chenshuang Li
  • Peter E.D. Love
  • Yuyue Gao

Some recent significant papers authored by Lieyun Ding include:

  • "Machine learning in construction: From shallow to deep learning," 2021, Developments in the Built Environment
  • "Construction quality information management with blockchains," 2020, Automation in Construction
  • "Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology," 2020, Automation in Construction
  • "Hyperledger fabric-based consortium blockchain for construction quality information management," 2020, Frontiers of Engineering Management
  • "Deep learning and network analysis: Classifying and visualizing accident narratives in construction," 2020, Automation in Construction

Best Publications

  • Detecting non-hardhat-use by a deep learning method from far-field surveillance videos

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

    Lieyun Ding;Weili Fang;Hanbin Luo;Peter E.D. Love

  • Building Information Modeling (BIM) application framework: The process of expanding from 3D to computable nD

    Lieyun Ding;Ying Zhou;Ying Zhou;Burcu Akinci

  • Falls from heights: A computer vision-based approach for safety harness detection

    Weili Fang;Lieyun Ding;Hanbin Luo;Peter E.D. Love

  • Non-linear description of ground settlement over twin tunnels in soil

    Ling Ma;Lieyun Ding;Hanbin Luo

  • Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach

    Weili Fang;Lieyun Ding;Botao Zhong;Peter E.D. Love

  • Computer vision for behaviour-based safety in construction: A review and future directions

    Weili Fang;Peter E.D. Love;Hanbin Luo;Lieyun Ding

  • Machine learning in construction: From shallow to deep learning

    Yayin Xu;Ying Zhou;Przemyslaw Sekula;Przemyslaw Sekula;Lieyun Ding

  • Construction risk knowledge management in BIM using ontology and semantic web technology

    L.Y. Ding;B.T. Zhong;Song Wu;H.B. Luo

  • Improved Fuzzy Bayesian Network-Based Risk Analysis With Interval-Valued Fuzzy Sets and D–S Evidence Theory

    Yue Pan;Limao Zhang;ZhiWu Li;Lieyun Ding

  • Computer vision applications in construction safety assurance

    Weili Fang;Lieyun Ding;Peter E.D. Love;Hanbin Luo

  • Real-time safety early warning system for cross passage construction in Yangtze Riverbed Metro Tunnel based on the internet of things

    L.Y. Ding;C. Zhou;Q.X. Deng;H.B. Luo

  • Construction quality information management with blockchains

    Da Sheng;Lieyun Ding;Botao Zhong;Peter E.D. Love

  • Ontology-based semantic modeling of regulation constraint for automated construction quality compliance checking

    B.T. Zhong;L.Y. Ding;H.B. Luo;Y. Zhou

  • Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

    Weili Fang;Ling Ma;Peter E.D. Love;Hanbin Luo

  • Digital reproduction of historical building ornamental components: From 3D scanning to 3D printing

    Jie Xu;Lieyun Ding;Peter E.D. Love

  • Safety barrier warning system for underground construction sites using Internet-of-Things technologies

    C. Zhou;L.Y. Ding

  • Application of 4D visualization technology for safety management in metro construction

    Y. Zhou;L.Y. Ding;L.J. Chen

  • Dynamic prediction for attitude and position in shield tunneling: A deep learning method

    Cheng Zhou;Hengcheng Xu;Lieyun Ding;Linchun Wei

  • Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • Hyperledger fabric-based consortium blockchain for construction quality information management

    Botao Zhong;Haitao Wu;Lieyun Ding;Hanbin Luo

  • A deep learning-based method for detecting non-certified work on construction sites

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • Development of web-based system for safety risk early warning in urban metro construction

    L.Y. Ding;C. Zhou

Frequent Co-Authors

Hanbin Luo
Hanbin Luo Huazhong University of Science and Technology
Miroslaw J. Skibniewski
Miroslaw J. Skibniewski University of Maryland, College Park
Peter E.D. Love
Peter E.D. Love Curtin University
Heng Li
Heng Li Hong Kong Polytechnic University
Limao Zhang
Limao Zhang Huazhong University of Science and Technology
Xiangyu Wang
Xiangyu Wang Curtin University
Yi-Qing Ni
Yi-Qing Ni Hong Kong Polytechnic University
Xiaoling Zhang
Xiaoling Zhang University of Hong Kong
Jim Smith
Jim Smith Bond University
Feniosky Peña-Mora
Feniosky Peña-Mora Columbia University

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