This book gives a rigorous and up-to-date study of the various AI and machine learning algorithms for resolving environmental challenges associated with blasting. Blasting is a critical activity in any mining or civil engineering project for breaking down hard rock masses. A small amount of explosive energy is only used during blasting to fracture rock in order to achieve the appropriate fragmentation, throw, and development of muck pile. The surplus energy is transformed into unfavourable environmental effects such as back-break, flyrock, air overpressure, and ground vibration. The advancement of artificial intelligence and machine learning techniques has increased the accuracy of predicting these environmental impacts of blasting. This book discusses the effective application of these strategies in forecasting, mitigating, and regulating the aforementioned blasting environmental hazards.

Autorentext

Dr. Ramesh M. Bhatawdekar is currently an adjunct professor in the Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India. He is also Head of Training and Courses at Geotropik, Department of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia. He obtained his Ph.D. in Civil- AI/ML application in blasting, from Universiti Teknologi Malaysia. His areas of research are in drilling, rock mechanics, rock mass classification and blasting environmental issues, application of artificial intelligence and optimization algorithms in geotechnics.

Dr. Danial Jahed Armaghani is currently working as a senior researcher in the Institute of Architecture and Construction at South Ural State University, Russia. 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 area of research is tunnelling, rock mechanics, piling technology, blasting environmental issues, applying articial intelligence, and optimization algorithms in civil-geotechnics. Dr. Danial published more than 200 papers in well-established ISI and Scopus journals, national, and international conferences. Dr. Danial is also a recognized reviewer in the area of rock mechanics and geotechnical engineering.

Dr. Aydin Azizi holds a Ph.D. 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,s and 'Exceptional Talent' recognition by the British Royal Academy of Engineering.

Inhalt
Chapter 1: An Overview of Blasting Operations and Possible Techniques to Solve Environmental Issues of Blasting Chapter 2: Machine Learning Techniques to Solve Problems Related to Rock Fragmentations Induced by Blasting

Chapter 3: Applications of AI and ML to Predict Back-Break and Flyrock Distance Resulting from Blasting Chapter 4: Blast-Induced Air and Ground Vibrations: A Review of Soft Computing Techniques