Method for intelligent guidance and operational quality control of underwater excavation based on digital twin technology
CSTR:
Author:
Affiliation:

(State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University)

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    To improve the quality of underwater excavation operations, this paper proposes a digital twin-based method for intelligent guidance and operational quality control of underwater excavation. By developing intelligent guidance hardware for the excavator and establishing the geometric relationships and kinematic equations of its components, efficient calculation of the bucket tip pose is achieved. A building information model (BIM) of the underwater excavation profile is established using Three.js and Unity 3D, and a digital twin model of the excavator is constructed by integrating real-time sensor data. Finally, based on real-time calculation of the relative distance between the bucket tip and the target excavation surface, an intelligent guidance and operational quality control strategy for excavation operations is developed, and the corresponding system is implemented. Case study results demonstrate that this method enables three-dimensional visualization of the excavation process and remote collaborative monitoring, provides intelligent guidance for operators to correct deviations, and ensures the quality of underwater excavation.

    Reference
    Related
    Cited by
Get Citation

王子健,刘东海,黄涛,等.基于数字孪生的水下挖掘智能引导与作业质量控制方法[J].水利水电科技进展,2026,46(3):121-128.(Wang Zijian, Liu Donghai, Huang Tao, et al. Method for intelligent guidance and operational quality control of underwater excavation based on digital twin technology[J]. Advances in Science and Technology of Water Resources,2026,46(3):121-128.(in Chinese))

Copy
Related Videos

Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:March 09,2025
  • Revised:
  • Adopted:
  • Online: June 01,2026
  • Published:
Article QR Code