Artificial Intelligence in Mechatronics and Civil Engineering

Bridging the Gap
1. Auflage 2023
ISBN: 978-981-19-8789-2
Verlag: Springer

Buch, Englisch, 247 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 606 g

Reihe: Emerging Trends in Mechatronics

Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Momeni, Ehsan

Jahed Armaghani, Danial

Azizi, Aydin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Mechatronik, Mikrosysteme (MEMS), Nanosysteme
• Geowissenschaften Geologie Geotechnik

Inhaltsverzeichnis

Chapter 1: A review on the application of soft computing techniques in Foundation Engineering – Ehsan Momeni, Iran 

Chapter 2: Damage Level Prediction of Concrete Structure using the Transfer Function variation of Artificial Neural Networks- Reni Suryanita, Indonesia

Chapter 3: Deep Foundation Bearing Capacity Analysis Subject to Field Testing (Static and Dynamic Pile Testing)-Harnedi Maizir, Indonesia

Chapter 4:  Feasibility of artificial intelligence techniques in rock characterization -Mohamad Bagher Dowlatshahi, Iran

Chapter 5: Application of optimized extreme gradient boosting in predicting blasted-induced back-break, Biao He, Malaysia

Chapter 6: Application of Artificial Intelligence Techniques for the Verification of Piling Capacity at Construction Site: A Review, Chia Yu Huat, Malaysia

Chapter 7: A nanostructure optical sensor for smart mechatronic systems, Ali Farmani, Iran

Chapter 8: The applicability of meta-heuristic optimization algorithms in optimal design of detention rockfill dams for flood coping under the effect of uncertainty, Iman Bahrami, Iran

Über Autor(innen)

Dr. Ehsan Momeni currently works as Assistant Professor in the Faculty of Engineering, Lorestan University, Iran. He received his postdoc from University of Tehran, Iran, and his Ph.D. degree, in Civil-Geotechnics, from Universiti Teknologi Malaysia, Malaysia. His areas of research are foundation engineering, reliability analysis, rock mechanics, and applied artificial intelligence.

Dr. Danial Jahed Armaghani is currently working as Research Associate at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Australia. He received his postdoc from Amirkabir University of Technology, Tehran, Iran, and his Ph.D. degree, in Civil Geotechnics, from Universiti Teknologi Malaysia, Malaysia. His areas of research are tunneling, rock mechanics, piling technology, blasting environmental issues, applying arti?cial intelligence, and optimization algorithms in different areas of civil engineering.

Dr. Aydin Azizi holds a Ph.D. degree in Mechanical Engineering-Mechatronics, an M.Sc. in Mechatronics, a B.Sc. in Mechanical Engineering-Heat & Fluids and certified as Official Instructor for the Siemens Mechatronic Certification Program (SMSCP). He currently serves as 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 Recipient of the National Research Award of Oman for his AI-focused research, DELL EMC’s “Envision the Future” competition award in IoT for “Automated Irrigation System”, and “Exceptional Talent” recognition by the British Royal Academy of Engineering.