Application of Soft Computing Techniques in Geotechnical Engineering and Risk Analysis : Proceedings of the Indian Geotechnical Conference 2023 / edited by Priti Maheshwari, Alok Bhardwaj, Vishwas A . Sawant.

Author
Maheshwari, Priti [Browse]
Format
Book
Language
English
Εdition
1st ed. 2025.
Published/​Created
Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Description
1 online resource (306 pages)

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Summary note
This book presents the select proceedings of the annual conference of the Indian Geotechnical Society 2023. The conference brings together researchers, practitioners, and academicians on various aspects of geotechnical and geoenvironmental engineering including application of soft computing techniques in geotechnical engineering, numerical modeling of various substructures, characterization of geomaterials, ground improvement techniques, rock mechanics and rock engineering, and risk analysis. This volume brings together cutting-edge research and practical applications from researchers in the field. Featuring insights on AI/ML integration, geoinformatics, and geohazard risk analysis, this book showcases how emerging technologies are transforming geotechnical problem-solving. With a strong focus on soft computing techniques and probabilistic methods, it addresses the critical role of uncertainty in geomaterials and substructure design. The contents of this book will not only be of interest to researchers but also to practicing engineers.
Source of description
Description based on publisher supplied metadata and other sources.
Contents
  • Artificial Intelligence Application in Geotechnical Engineering A Review
  • Machine Learning Based Earthquake Prediction Model A Comparative Study of Time Series Analysis and Conventional Algorithms
  • Prediction of the Critical Failure Surface and Factor of Safety of Finite Slopes Using Machine Learning Algorithms
  • Comparing Machine Learning Techniques for Accurate Prediction of Unconfined Compressive Strength of Fine-Grained Soil
  • Post event landslide detection using ResU Net model
  • A Comparative Study for Predicting Standard Penetration Number Through ML Techniques.
ISBN
9789819695294 ((electronic bk.))
OCLC
1549520823
Doi
  • 10.1007/978-981-96-9529-4
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