AI-Enhanced Safety Evaluation for Tunnelling in Rock

Principles, Methods and Algorithms
1. Auflage 2025
ISBN: 978-1-032-97606-8
Verlag: Taylor & Francis Ltd

Buch, Englisch, 304 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g

Reihe: Challenges in Geotechnical and Rock Engineering

AI techniques for rock tunnel construction offer innovative solutions for assessing rock mass quality and ensuring excavation safety in challenging geological conditions. Cutting-edge contact methods and non-contact methods such as digital photography can both provide continuous geological data during excavation. Then advanced deep learning algorithms for precise characterization of rock face features, along with pioneering multi-source 3D data fusion modelling, can enable refined rock mass classification and sophisticated safety evaluation techniques tailored to complex geological environments. By integrating machine vision and intelligent algorithms with rigorous statistical analysis and machine learning models, this book provides practical and refined solutions for the construction industry. It offers improved safety, efficiency, and reliability for tunnel projects, and serves as a valuable reference for graduate students and academics.

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Huang, Hongwei

Chen, Jiayao

Zhou, Mingliang

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik
• Technische Wissenschaften Bauingenieurwesen Baukonstruktion, Baufachmaterialien

Inhaltsverzeichnis

1. Introduction 2. Rock Mass Data Acquisition and Classification using Digital Photography 3. Automated Classification of Rock Mass Structural Features 4. Extraction and Quantitative Characterization of Weak Interlayers 5. Characterization of Groundwater Inflow 6. Extraction and Handling of Data on Joint Fractures 7. Characterization of Three-Dimensional Discontinuities and Traces 8. Intelligent Rock Mass Classification from Data Fusion 9. Tunnel Excavation Safety Assessment Based on Machine Vision and 3DEC Simulation 10. Conclusion and Outlook

Über Autor(innen)

Dr. Jiayao Chen is Associate Professor at Beijing Jiaotong University. He is a board member and secretary of the Chinese Society of Civil Engineering Risk and Insurance Research Division.

Prof. Hongwei Huang is a distinguished professor at Tongji University, China, a "Yangtze Scholar," and President of the Engineering Risk and Insurance Research Branch of the Chinese Civil Engineering Society. He also leads the International Joint Research Center for Resilient Infrastructure.

Dr. Mingliang Zhou is an Associate Professor and Assistant Dean at the College of Civil Engineering, Tongji University and a recipient of the International Postdoctoral Exchange Fellowship. He earned his Ph.D. from Cambridge University, UK, and is a core member of ISSMGE's YMPG and TC309.