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Zili Li

Senior Lecturer (Associate Professor) at University College Cork

University College Cork
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Ireland

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Dr. Zili Li is a Senior Lecturer (Associate Professor) at University College Cork, Ireland. His research focuses on advanced engineering solutions and innovative technologies for infrastructure, including earthquake-resistant pipelines, tunnel performance modeling, and drone-based geological conservation. Recent publications highlight his work on optimization methods for structural resilience and the application of deep learning in infrastructure monitoring.

Positions (1)

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Zili Li

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University College Cork

PhD in Civil, Computer, Digital, or Tunnelling Engineering: AI for Ground-Tunnel Interaction and Infrastructure Maintenance

This PhD opportunity at University College Cork (UCC), in collaboration with CERN, focuses on the development of Large Language Models and AI for ground-tunnel interaction and infrastructure maintenance. The project aims to address the challenge of performance degradation in large-scale underground infrastructure, such as tunnels and pipelines, by integrating diverse data sources including crack images, ground-penetrating radar (GPR) images, continuous strain sensor data, and historical borehole logs. The research will develop a systematic framework to correlate these data sources with the condition of concrete linings, providing insights into the long-term behavior of underground structures under varying hydrogeological conditions. The supervision team includes Dr. Zili Li from UCC and collaborators at CERN, offering a unique interdisciplinary environment at the intersection of civil engineering, computer science, and artificial intelligence. The ideal candidate will have a strong background in civil engineering or computer science, with experience in structural health monitoring and machine learning considered advantageous. Applicants must hold at least a 2.1 honours Bachelor's degree or equivalent and demonstrate English language proficiency if required. Currently, a 50% PhD scholarship is available, with the possibility of additional funding based on academic progress. The application process requires submission of a CV (maximum 2 pages) and a cover letter detailing relevant experience and motivation. This project is ideal for candidates interested in AI applications in infrastructure, data integration, and the future of underground construction and maintenance. For more information, refer to the relevant publication: Image-based large language model approach to road pavement monitoring .

Articles (15)

Monitoring and assessment of a cross-passage twin tunnel long-term performance using wireless sensor network

The monitoring and assessment of ageing underground tunnels is critical to ensure their serviceability, stability, and safety as arteries for a transport network in the long term. This paper first comprehensively reviewed the long-term tunnel performance monitoring case studies, aimed at highlighting the limited field monitoring data and identifying research gaps. It was found that previous studies largely focused on the performance of single tunnel sections subject to short-term disturbances (e.g., adjacent excavation), whereas limited efforts concentrated on the long-term performance of twin tunnels, let alone those with cross passages, under the influence of deteriorations. To this end, a Wireless Sensor Network (WSN) was deployed at a critical vehicle cross passage (VCP) twin tunnel section of Dublin Port Tunnel to monitor its long-term ageing performance with time, in addition to the existing long-term water leakage and lining crack monitoring. The evolvement of lining crack and water leakage since 2010 indicated the progressive deteriorations of the monitoring section, and the deployed WSN monitoring of lining inclination demonstrated a robust sensor deployment layout and monitoring plan for (quasi) real-time monitoring for a confined underground cross passage twin tunnel network. An analytical solution was proposed to convert tunnel inclination to horizontal deformation, with the converted measurements suggesting that even more than one decade after construction, both twin tunnels are still moving horizontally towards the VCP centreline in the long term, primarily due to twin tunnel interaction. Along tunnel longitudinal direction, the closer to the VCP, the greater tunnel deformation rate is, revealing the effect of cross passage on tunnel differential longitudinal behaviour, in agreement with hypotheses and numerical results in previous studies. The field observations were believed to be attributed to the two mechanisms which are hydro-geological degradation of the surrounding ground and hydro-mechanical deterioration of the tunnel, where the correlation between tunnel deformation and deteriorations was detailed.

Year:

2023

Collaborators (2)

Jimmy Murphy

University College Cork

IRELAND

Jorge Emanuel Ramalho da Fonseca

University of Aveiro

PORTUGAL
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