Problematics of Artificial Intelligence at the World Tunnelling Congress 2024: Review

The 50th World Tunnelling Congress reflects the state of the art in underground construction research, including artificial intelligence based on digitalisation.

The review is based on the published proceedings of the Congress. The subject of artificial intelligence is reflected in a separate section.

The set of reports presented at the Congress can be divided into the following several areas: operation of tunnel boring machines; study of ground and structure deformations, monitoring; training methods; digital methods of design, construction and operation management.

An intelligent decision support system for the maintenance of structural defects in tunnels using knowledge graph and deep learning is proposed, and an experimental study of the application of hyperspectral images to assess the compressive strength of concrete is presented. Prediction of surrounding rock quality based on an incomplete data set from multiple sources and the application of simple Bayesian network trees is described. The design, model selection and uncertainty quantification of soil conditioning for mechanised tunnel sinking using machine learning are discussed. An energy-efficient tunnel ventilation control algorithm combining the application of dynamic neural network and fuzzy control is recommended. Special attention is paid to the performance evaluation of tunnel boring machines and defect detection of operating underground facilities. In general, the scope of the presented research in the fi eld of artificial intelligence (machine learning) as applied to the development of underground space is relatively narrow and leaves many aspects uncovered.

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23. Samadi H., Hassanpour J., Rostami J., Moghbeli A. Assessment of TBM performance in different types of rocks using supervised learning techniques. Tunnelling for a Better Life. 2024;3095-3103.

24. Yuan X., Wang S., Qu T. Machine learning-informed soil conditioning for mechanized shield tunneling feature engineering, model selection, and uncertainty quantification. Tunnelling for a Better Life. 2024;3139-3145.

25. Pei L., Wu H., Hu M., Lu J., Wu B. et al. Research and practice of digital lean construction mode of tunnelling based on shield self-driving technology. Tunnelling for a Better Life. 2024;3079- 3085.

26. Sakai K., Miyanaga S., Yamagami M. Study on machine learning method for supporting conventional tunnel engineering judgement. Tunnelling for a Better Life. 2024;3086-3094.

27. Wang H., Li Z., Zhang Y., Zhang J. An energy-effi cient tunnel ventilation control algorithm combining dynamic neural network and fuzzy control. Tunnelling for a Better Life. 2024:3113-3121.