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Khabbaz / Rujikiatkamjorn / Tamadon Advances in Geomechanics and Geotechnical Engineering
Erscheinungsjahr 2025
ISBN: 978-981-968697-1
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of the 2024 AGS Sydney Annual Symposium
E-Book, Englisch, 281 Seiten
Reihe: Engineering
ISBN: 978-981-968697-1
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents the selected papers from the 28th Annual Symposium organised by the Sydney Chapter of the Australian Geomechanics Society (AGS). The symposium brought together key stakeholders from the Australian geological and geotechnical community. The book showcases state-of-the-art practices, design approaches, recent research findings, and case histories covering a wide range of topics, including emerging technologies in ground improvement, advanced numerical models for soil consolidation, applications of machine learning, rock modelling, slope stability risk management, and innovative and sustainable considerations in design and construction. It also highlights the application of recent in-situ testing equipment and methodologies. Furthermore, the book discusses recent innovations, trends, concerns, practical challenges encountered, and solutions adopted within the field. It serves as a valuable reference for academics and industry professionals engaged in geotechnical engineering. This publication represents the collaborative efforts of numerous authors and qualified reviewers.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Chapter 1. Apply Probabilistic Methods for Slope Stability Analysis.- Chapter 2. Reliability Based Design: An Australian Experience.- Chapter 3. Potential use of As5104 for Reliability Based Design.- Chapter 4. Optimising Site Investigations using Monte Carlo Analysis and Genetic Algorithms.- Chapter 5. The Random Finite Element Method, its Implementation in Geotechnical Software through Python, and a Comparison with the Random Limit Equilibrium Method.- Chapter 6. Application of Machine Learning Methods in Estimating Soil Parameters from Dynamic Penetration Tests.- Chapter 7. A Case Study of an Impact Assessment of an Excavation in the Sydney Cbd, Adopting Industry Guidance for Numerical Modelling.- Chapter 8. Bayesian Analysis of Consolidation Parameters of a Tailings Storage Facility.- Chapter 9. Cognitive Biases and their Influence on Projects.




