Title

Big data and machine learning in life-cycle design, construction and maintenance of tunnel and underground engineering

 

Special session organizers

Prof. Dongming Zhang, Tongji University  

Prof. Zhenyu Yin, the Hong Kong Polytechnic University

Prof. Hongwei Huang, Tongji University

 

Overview

Thousands of miles of tunnels have been built all over the world, and more miles of tunnels will be built. Due to complex geological conditions and changeable surrounding environment, a large number of highly repetitive and professional calculation, manipulation, monitoring and evaluation work are required in the design, construction, operation and maintenance stages of tunnels. How to effectively use historical data to better guide life-cycle design, construction and maintenance of tunnel and underground engineering is very important to promote the digital transformation of tunnel and underground engineering and improve the efficiency of production and construction.

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Recently, big data technology and machine learning methods have brought changes to all walks of life. In underground engineering, a large number of geological exploration databases provide references for the prediction of soil parameters and structural design. The large amount of data collected in mechanized tunnel construction is helpful for establishing the machine learning model of the relationship between various parameters. In the era of big data, the means of machine learning can help us mine the relationship between the data of life-cycle design, construction and maintenance of tunnel and underground engineering, better understanding the ground-tunnel interaction. In view of the broad needs of tunnel design, construction and maintenance, it is necessary to study the structured method of data of life-cycle design, construction and maintenance of tunnel and underground engineering, the application method of machine learning algorithm, the introduction of human knowledge and others.

 

 

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:

• Data mining and application of geological exploration

• Machine learning and data driven based soil modeling

• Establishment and management of database for underground infrastructure

• Intelligent underground infrastructure design

• Automatic construction of underground infrastructure

• Advanced monitoring and data analysis method of underground infrastructure

• Techniques for data fusion of multiple sources

• Application of unmanned aerial vehicle, image recognition, laser scanning, 3D reconstruction and virtual reality for tunnels

• Design, construction, maintenance of tunnels and underground engineering during life cycle 

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