Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience

The book explores the application of soft computing techniques, machine learning approaches, deep learning algorithms, and optimization techniques in geoengineering and geoscience, with a focus on tunnelling, excavation, pipelines, and geohazards. It features state-of-the-art studies and is targeted at students, industry personnel, and practitioners.

Format: Paperback / softback
Length: 138 pages
Publication date: 14 October 2022
Publisher: Springer Verlag, Singapore

The book "Soft Computing Techniques, Machine Learning Approaches, Deep Learning Algorithms, and Optimization Techniques in Geoengineering and Geoscience" provides a comprehensive overview of the application of soft computing techniques, machine learning approaches, deep learning algorithms, and optimization techniques in geoengineering and geoscience. It covers a wide range of topics, including tunnelling, excavation, pipelines, and geohazards, and explores the use of SC, ML, DL, and optimizations in various aspects of these fields.

One of the key features of the book is its state-of-the-art studies on the use of SC, ML, DL, and optimizations in geoengineering and geoscience. These studies provide valuable insights into the latest developments and advancements in these areas and highlight the potential benefits of using these techniques in real-world applications.

The book is designed to cater to a wide audience, including students of undergraduate, postgraduate, and research scholars, as well as industry personnel and practitioners. It provides a clear and concise explanation of the concepts and techniques, and includes numerous examples and case studies to illustrate their practical applications.

The book is divided into several chapters, each focusing on a specific aspect of geoengineering or geoscience. The chapters are written by experts in the field and provide a comprehensive overview of the topic, including its historical background, current research, and future prospects.

In the chapter on tunnelling, the authors discuss the use of SC, ML, DL, and optimizations in designing and constructing tunnels. They cover topics such as tunnel optimization, tunnel ventilation, and tunnel safety, and provide examples of successful tunnel projects that have used these techniques.

In the chapter on excavation, the authors discuss the use of SC, ML, DL, and optimizations in excavating soil and rock. They cover topics such as soil classification, rock strength, and excavation planning, and provide examples of successful excavation projects that have used these techniques.

In the chapter on pipelines, the authors discuss the use of SC, ML, DL, and optimizations in designing and constructing pipelines. They cover topics such as pipeline routing, pipeline design, and pipeline safety, and provide examples of successful pipeline projects that have used these techniques.

In the chapter on geohazards, the authors discuss the use of SC, ML, DL, and optimizations in identifying and mitigating geohazards. They cover topics such as landslides, earthquakes, and volcanic eruptions, and provide examples of successful geohazard mitigation projects that have used these techniques.

In the chapter on rock and soil properties, the authors discuss the use of SC, ML, DL, and optimizations in characterizing rock and soil properties. They cover topics such as rock strength, soil permeability, and soil erosion, and provide examples of successful rock and soil property characterization projects that have used these techniques.

One of the key advantages of using soft computing techniques, machine learning approaches, deep learning algorithms, and optimization techniques in geoengineering and geoscience is that they can help to improve the efficiency, safety, and sustainability of these fields. For example, SC, ML, DL, and optimizations can be used to optimize the design of tunnels, excavations, and pipelines, which can reduce the cost of construction and improve the safety of workers and the public.

In addition, SC, ML, DL, and optimizations can be used to identify and mitigate geohazards, which can help to protect human lives and property. For example, SC, ML, DL, and optimizations can be used to predict landslides, earthquakes, and volcanic eruptions, which can allow for early warning and evacuation.

However, the use of soft computing techniques, machine learning approaches, deep learning algorithms, and optimization techniques in geoengineering and geoscience also presents several challenges. For example, the data used in these techniques can be complex and difficult to analyze, and the algorithms used can be sensitive to small changes in the data.

To address these challenges, the book provides a comprehensive set of tools and techniques for analyzing and optimizing data. It includes chapters on data preprocessing, feature extraction, model selection, and model evaluation, and provides examples of successful applications of these techniques in geoengineering and geoscience.

In conclusion, the book "Soft Computing Techniques, Machine Learning Approaches, Deep Learning Algorithms, and Optimization Techniques in Geoengineering and Geoscience" is a valuable resource for students, industry personnel, and practitioners in the fields of geoengineering and geoscience. It provides a comprehensive overview of the application of soft computing techniques, machine learning approaches, deep learning algorithms, and optimization techniques in these fields, and highlights the potential benefits of using these techniques in real-world applications. By providing a clear and concise explanation of the concepts and techniques, and including numerous examples and case studies, the book helps to make these fields more accessible and understandable to a wide audience.

Weight: 244g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811668371
Edition number: 1st ed. 2022