Ruilin Liu

Ruilin Liu

Higher Degree by Research Candidate

School of Architecture and Civil Engineering

Faculty of Sciences, Engineering and Technology

Ruilin Liu is a first year PhD focusing on intelligent monitoring and risk management in underground infrastructure. Research includes smart water network optimization, AIoT-based pipeline leak analysis, and tunnel defect detection.

  • Optimization and Condition Assessment of Smart Water Supply Networks
    Researching methods to enhance the efficiency and reliability of water supply systems by analyzing network status, predicting failures, and optimizing operations using smart algorithms.
  • Pipeline Leak Detection and Data Analysis Based on AI and IoT Technologies
    Integrating artificial intelligence with IoT sensors to monitor pipeline systems in real time, detect leaks early, and analyze operational data for smarter decision-making.
  • Risk Management and Health Monitoring in Underground Engineering
    Focused on identifying and evaluating potential risks in underground infrastructure projects, with an emphasis on improving safety and long-term performance through health monitoring technologies.
  • Deep Learning for Tunnel Defect Detection and Intelligent Diagnosis
    Developing advanced deep learning models to automatically detect and classify tunnel defects from images or sensor data, enabling faster and more accurate maintenance decisions.

  • Language Competencies
    Language Competency
    Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
    English Can read, write, speak, understand spoken and peer review

  • Journals
    Year Citation
    2024 Liu, R., & Zeng, W. (2024). Automatic detection of structural defects in tunnel lining via network pruning and knowledge distillation in YOLO. Structural Health Monitoring, 17 pages.
    DOI
    2022 Man, K., Liu, R., Liu, X., Song, Z., Liu, Z., Cao, Z., & Wu, L. (2022). Water Leakage and Crack Identification in Tunnels Based on Transfer-Learning and Convolutional Neural Networks. WATER, 14(9), 15 pages.
    DOI
    2022 Man, K., Liu, X., Song, Z., Liu, Z., Liu, R., Wu, L., & Cao, Z. (2022). Dynamic Compression Characteristics and Failure Mechanism of Water-Saturated Granite. WATER, 14(2), 12 pages.
    DOI
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