Description
Book SynopsisThis 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.
Table of ContentsChapter 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
