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Applications of Artificial Intelligence in Tunnelling and Underground Space Technology [electronic resource] / by Danial Jahed Armaghani, Aydin Azizi.

By: Jahed Armaghani, Danial [author.].
Contributor(s): Azizi, Aydin [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Applied Sciences and Technology: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: IX, 70 p. 16 illus., 15 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811610349.Subject(s): Engineering geology | Statistical Physics | Geotechnical engineering | Mathematical statistics | Manufactures | Engineering mathematics | Geoengineering | Statistical Physics | Geotechnical Engineering and Applied Earth Sciences | Mathematical Statistics | Machines, Tools, Processes | Engineering MathematicsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 624.15 Online resources: Click here to access online
Contents:
Chapter 1. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance -- Chapter 2. Empirical, Statistical and Intelligent Techniques for TBM Performance Prediction. Chapter 3. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem -- Chapter 4. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones.
In: Springer Nature eBookSummary: 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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Chapter 1. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance -- Chapter 2. Empirical, Statistical and Intelligent Techniques for TBM Performance Prediction. Chapter 3. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem -- Chapter 4. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones.

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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