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Special Session Proposal for ISMLG 2023

Title

Big data and machine learning for ageing tunnels and underground infrastructures

 

Overview

Thousands of miles of tunnels have been constructed across the globe and many of them have been in operation for decades. Due to complex time-varying geological conditions, structural deterioration and other causes, old tunnels usually degrade as evidenced by deformation, cracks, water leakage and other defects, which are of great engineering concerns. In the light of tunnel safety & serviceability, it is essential to introduce new monitoring technologies to better understand the behaviour of tunnels and then conduct condition assessments for tunnel maintenance and decision-making.

Recently, emerging new digital tools, like remote sensing and image recognition, can gather enormous amounts of data of tunnel performance and surrounding ground condition, such as the structural response of tunnel linings, subsurface geology, hydrogeology and geotechnical characteristics. In the era of ‘big data’, the databases have enormous potentials to enhance our understanding of ground-tunnel interaction and provide early warnings of the potential casualty and financial loss. Given the ever-increasing amount of underground infrastructure data in the next decades, it is necessary to develop advanced data fusion and analytical procedures to process, analyze and manage the incoming ‘big data’ available from all deployed sensor systems and monitoring tools.

 

Special session organizers: Prof. Asaad Faramarzi, Prof. Fei Ye, Dr. Zhipeng Xiao

 

Topics

This special session invites authors to submit high quality research papers and deliver presentations on the following topics that include (but are not limited to) the follows:

  • Novel monitoring tool and techniques for underground space

  • Methods of data interpretation and analysis for underground infrastructure

  • Methods of safety assessment of ageing tunnel

  • Tunnel maintenance strategy

  • Methods of data mining in tunnel engineering

  • Techniques for data fusion of multiple sources

  • Application of machine learning for tunnels

  • Design, construction, maintenance of tunnels during life cycle 

  • Development and application of virtual reality for tunnels