Applications of Artificial Intelligence in Tunnelling and Underground Space Technology
Springer Verlag, Singapore
978-981-16-1033-2 (ISBN)
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.
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.
Chapter 1. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance.- Chapter 2. Empirical, Statistical and Intelligent Techniques for TBM Performance Prediction. Chapter 3. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem.- Chapter 4. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones.
| Erscheinungsdatum | 19.03.2021 |
|---|---|
| Reihe/Serie | SpringerBriefs in Applied Sciences and Technology |
| Zusatzinfo | 15 Illustrations, color; 1 Illustrations, black and white; IX, 70 p. 16 illus., 15 illus. in color. |
| Verlagsort | Singapore |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Naturwissenschaften ► Geowissenschaften ► Geologie |
| Naturwissenschaften ► Geowissenschaften ► Meteorologie / Klimatologie | |
| Naturwissenschaften ► Physik / Astronomie ► Thermodynamik | |
| Technik ► Bauwesen | |
| Schlagworte | Boring machines • Empirical Models • Intelligent computational techniques • optimization algorithms • predictive models • Statistical Techniques • tunneling |
| ISBN-10 | 981-16-1033-9 / 9811610339 |
| ISBN-13 | 978-981-16-1033-2 / 9789811610332 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
