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El. knyga: Uncertainty, Modeling, and Decision Making in Geotechnics

Edited by (Singapore University of Technology and Design, Singapore), Edited by (Okayama University, Japan), Edited by (National Taiwan University, Taipei)
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"This book shows how uncertainty quantification and numerical modeling can complement each other to enhance decision making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly. It helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. Bringing together statistical characterization, reliability analysis, reliability-baseddesign, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community"--

Uncertainty, Modeling, and Decision Making in Geotechnics shows how uncertainty quantification and numerical modeling can complement each other to enhance decision-making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly.

The book helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. In particular, it provides guidance on the selection of realistic statistics and a cost-effective, accessible method to address different design objectives, and for different problem settings, and illustrates the value of this to decision-making using realistic examples.

Bringing together statistical characterization, reliability analysis, reliability-based design, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community.



This reference guide from an international team of experts shows how uncertainty quantification and numerical modeling can complement each other to enhance decision making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly.

Recenzijos

'All chapters of this book collectively highlight the growing importance of probabilistic and advanced computational methods in geotechnical engineering, focusing on the necessity to account for soil variability and construction uncertainties. Techniques like the Random Finite Element Method, Monte Carlo simulations, and reduced-order models are emphasised for their role in improving design accuracy and reliability. For practicing engineers, this signifies a shift towards more informed decision-making, allowing for safer, more efficient, and sustainable solutions. The inclusion of practical case studies makes the book a valuable guide for engineers to implement these methods in real-world scenarios.'

Behzad Fatahi in Georisk

'A strength of the book is its large scope in terms of probabilistic methods and applications, facilitated by the combined expertise of the contributing authors. You will find practical examples of piling, tunnels, footings, ground improvement, and slopes in both layered soil and jointed rock, to mention just a few... The premise is that the engineers decision-making can be enhanced by performing uncertainty quantification and numerical modelling in combination.'

Johan Spross in Structural Safety

'This book is a valuable resource to the practice of geotechnical engineering as problems become more challenging due to rapid urbanization, aging infrastructure, and climate change... This is a much-needed book to fill a gap in the literature regarding data-driven approaches that capitalize on digital transformation and vast advancements in computing power.'

Anand Govindasamy in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems

1. Uncertainty-informed decision making in Burland Triangle.
2. Soil and rock parametric uncertainties.
3. Uncertainty in constitutive models.
4. Uncertainty in data-driven site characterization.
5. Variability of predictions in geotechnics.
6. Geotechnical reliability analysis for practice.
7. Reliability analysis with reduced order model.
8. Stochastic finite element methods for slope stability analysis and risk assessment.
9. Reliability-based design with numerical models.
10. Probabilistic inverse analysis for geotechnics.
11. Use of geotechnical software for probabilistic analysis and design.
12. Reliability-based decision-making with FE models for real-life case studies.
13. Soil-structure interaction in spatially variable ground.
14. Reduction of uncertainty through piling data within the same site in the press-in piling method.
15. Slope reliability assessments for linear infrastructures.
16. Uncertainty, modelling, and decision making for ground improvement.

Kok-Kwang Phoon

is Cheng Tsang Man Chair Professor and Provost at the Singapore University of Technology and Design. He has edited three books and authored one book: Model Uncertainties in Foundation Design (CRC Press, 2021). He was awarded the ASCE Norman Medal twice, in 2005 and in 2020, and the Humboldt Research Award in 2017. He is the Founding Editor of Georisk and appointed a Board Member of ISSMGE and elected as a Fellow of the Academy of Engineering Singapore.

Takayuki Shuku

is Associate Professor of Okayama University in Japan. He received the Best Paper Award from Japan Society of Civil Engineering in 2020, and the ISSMGE Bright Spark Lecture Award in 2019.

Jianye Ching

is Distinguished Professor at National Taiwan University and Convener of the Civil & Hydraulic Engineering Program of the Ministry of Science and Technology of Taiwan. He served as Chair of ISSMGEs TC304 (risk) and Chair of Geotechnical Safety Network (GEOSNet). He is Managing Editor of the journal Georisk.
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