Overview
- Presents statistical and intelligent computational techniques to calculate the performance of tunnel boring machine (TBM)
- Includes a review of available TBM performance predictive models in detail
- Introduces predictive models that are powerful and easy to implement, in estimating TBM performance parameters
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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Table of contents (4 chapters)
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Front Matter
Keywords
About this book
This book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, powerful and easy to implement, in estimating TBM performance parameters. The introduced models are accurate enough and they can be used for prediction of TBM performance in practice before designing TBMs.
Authors and Affiliations
About the authors
Danial Jahed Armaghani: I, currently work as a senior lecturer in the Faculty of Engineering, University of Malaya, Malaysia. I received my postdoc from Amirkabir University of Technology, Tehran, Iran and my Ph.D degree, in Civil-Geotechnics, from Universiti Teknologi Malaysia, Malaysia. My area of research is tunnelling, rock mechanics, piling technology, blasting environmental issues, applying artificial intelligence and optimization algorithms in geotechnics. I have published more than 100 papers in well-established ISI and Scopus journals, national and international conferences.
Dr. Aydin Azizi holds a PhD degree in Mechanical Engineering. Certified as an official instructor for the Siemens Mechatronic Certification Program (SMSCP), he currently serves as a Senior Lecturer at the Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control and optimize complex systems. Dr. Azizi’s areas of expertise include Control & Automation, Artificial Intelligence and Simulation Techniques. Dr. Azizi is the recipient of the National Research Award of Oman for his AI-focused research, DELL EMC’s “Envision the Future” completion award in IoT for “Automated Irrigation System”, and ‘Exceptional Talent’ recognition by the British Royal Academy of Engineering.
Bibliographic Information
Book Title: Applications of Artificial Intelligence in Tunnelling and Underground Space Technology
Authors: Danial Jahed Armaghani, Aydin Azizi
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-981-16-1034-9
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Softcover ISBN: 978-981-16-1033-2Published: 14 March 2021
eBook ISBN: 978-981-16-1034-9Published: 13 March 2021
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: IX, 70
Number of Illustrations: 1 b/w illustrations, 15 illustrations in colour
Topics: Geoengineering, Foundations, Hydraulics, Theoretical, Mathematical and Computational Physics, Geotechnical Engineering & Applied Earth Sciences, Probability Theory and Stochastic Processes, Applications of Mathematics, Statistical Theory and Methods