Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. This study is aimed to compare the ground vibrations predicted from empirical formula and analytical program with the real data. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. To evaluate and calculate the blast-induced ground vibration by incorporating blast design and rock strength, artificial neural networks (ANN) was used.

"Blasting has been widely used as an economical and cheap way of rock breakage in mining and civil engineering applications. An optimal blast yields the best fragmentation in a safe, economic and environmentally friendly manner. The degree of fragmentation is vital as it determines to a large extent the utilization of equipment, productivity and mill throughput. Explosive energy, besides rock fragmentation, creates health and safety issues such as ground vibration, air blast, fly rock, and back breaks among others. As a result, the explosive energy impacts structures and buildings located in the vicinity of the blasting operation, and causes human annoyance, as well as exposes operators in the field to hazardous conditions. There is therefore a need to develop a model to predict blast-induced ground vibration (PPV), airblast (AOp), and rock fragmentation. Artificial neural network (ANN) technique is preferred over empirical and other statistical predictive methods as it is able to incorporate the numerous factors affecting the outcome of a blast. This study seeks to develop a simultaneous integrated prediction model for rock fragmentation, ground vibration and air blast using MATLAB-based artificial neural network system. Training, validation and testing was done with a total of 180 monitored blast records taken from a gold mining company in Ghana using a three-layer, feed-forward back-propagation ANN. Based on the results obtained from the study, ANN model with architecture of 7-13-3 was found optimum having the least root mean square error (RMSE) of 0.307. Artificial neural network (ANN) technique has been compared to empirical and conventional statistical methods. Sensitivity analysis has also been conducted to ascertain the relative influence of each input parameter on rock fragmentation, PPV and AOp"--Abstract, page iii.

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.

This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.