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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 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.
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