Danial Jahed Armaghani – författare

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.

Colour versions of the figures in this book can be found at www.routledge.com/9781032373379.

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.

Colour versions of the figures in this book can be found at www.routledge.com/9781032373379.

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.   

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.

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.