Geotechnical Reliability Analysis
Theories, Methods and Algorithms
(Sprache: Englisch)
This textbook systematically introduces the theories, methods, and algorithms for geotechnical reliability analysis. There are a lot of illustrative examples in the textbook such that readers can easily grasp the concepts and theories related to...
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Produktinformationen zu „Geotechnical Reliability Analysis “
Klappentext zu „Geotechnical Reliability Analysis “
This textbook systematically introduces the theories, methods, and algorithms for geotechnical reliability analysis. There are a lot of illustrative examples in the textbook such that readers can easily grasp the concepts and theories related to geotechnical reliability analysis. A unique feature of the textbook is that computer codes are also provided through carefully designed examples such that the methods and the algorithms described in the textbook can be easily understood. In addition, the computer codes are flexible and can be conveniently extended to analyze different types of realistic problems with little additional efforts.
Inhaltsverzeichnis zu „Geotechnical Reliability Analysis “
- Chapter 1: Basics of probability theory- 1.1 Set theory
- 1.2 Conditional probability- 1.3 Total probability theorem
- 1.4 Discrete random variables- 1.5 Continuous random variables
- 1.6 Multivariate random variables- 1.7 Summary and further readings
- Chapter 2: First order reliability methods
- 2.1 Concept of geotechnical reliability
- 2.2 Mean first order reliability method (MFORM)
- 2.3 Advanced first order reliability method (AFORM)
- 2.4 AFORM-based system reliability analysis
- 2.5 Summary and further readings
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- Chapter 3: Simulation-based methods
- 3.1 Methods for generating random numbers
- 3.2 Estimating failure probability based on Monte Carlo simulation
- 3.3 Latin hypercube
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sampling
- 3.4 Importance sampling
- 3.5 Subset sampling
- 3.6 Summary and further readings
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- Chapter 4: Response surface methods
- 4.1 Classical response surface
- 4.2 Kriging-based response surface
- 4.3 Support vector machine-based response surface
- 4.4 Summary and further readings
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- Chapter 5: Modeling of spatial variability
- 5.1 Spatial variability of soils
- 5.2 Random field theory
- 5.3 Simulation of univariate random fields
- 5.4 Simulation of multivariate random fields
- 5.5 Effect of spatial variability on geotechnical reliability
- 5.6 Summary and further readings
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- Chapter 6: Reliability-based design in geotechnical engineering
- 6.1 Calibrating a single resistance factor
- 6.2 Calibrating multiple resistance factors
- 6.3 Challenges in implementing load resistance factor design in geotechnical engineering
- 6.4 Expanded reliability based design
- 6.5 Robust geotechnical design
- 6.6 Summary and further readings
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- Chapter 7: Bayesian methods
- 7.1 Concept of Bayesian updating
- 7.2 Conjugate prior distributions
- 7.3 Direct integration method
- 7.4 Maximum posterior density method
- 7.5 System identification method
- 7.6 Markov chain Monte Carlo simulation - 7.7 Summary and further readings
- 3.4 Importance sampling
- 3.5 Subset sampling
- 3.6 Summary and further readings
-
- Chapter 4: Response surface methods
- 4.1 Classical response surface
- 4.2 Kriging-based response surface
- 4.3 Support vector machine-based response surface
- 4.4 Summary and further readings
-
- Chapter 5: Modeling of spatial variability
- 5.1 Spatial variability of soils
- 5.2 Random field theory
- 5.3 Simulation of univariate random fields
- 5.4 Simulation of multivariate random fields
- 5.5 Effect of spatial variability on geotechnical reliability
- 5.6 Summary and further readings
-
- Chapter 6: Reliability-based design in geotechnical engineering
- 6.1 Calibrating a single resistance factor
- 6.2 Calibrating multiple resistance factors
- 6.3 Challenges in implementing load resistance factor design in geotechnical engineering
- 6.4 Expanded reliability based design
- 6.5 Robust geotechnical design
- 6.6 Summary and further readings
-
- Chapter 7: Bayesian methods
- 7.1 Concept of Bayesian updating
- 7.2 Conjugate prior distributions
- 7.3 Direct integration method
- 7.4 Maximum posterior density method
- 7.5 System identification method
- 7.6 Markov chain Monte Carlo simulation - 7.7 Summary and further readings
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Autoren-Porträt von Jie Zhang, Te Xiao, Jian Ji, Peng Zeng, Zijun Cao
Dr. Jie Zhang is a Professor from the Department of Geotechnical Engineering, Tongji University, China. His research mainly focuses on georisk assessment and management. He is one of the founding managing editors of the journal of Underground Space, an editorial board member of the journal of Georisk, and is the secretary of TC304 (Engineering Practice of Risk Assessment and Management) in the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). He is the recipient of the Young Researcher Award from GEOSNet and the instructor of the ISSMGE online class Probability Analysis in Civil Engineering. Te Xiao is currently a Research Assistant Professor of Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology (HKUST). He earned his PhD from Wuhan University in 2018. His main research interests include geotechnical uncertainty and risk, probabilistic site characterization, landslide and flooding risk, and machine learning and digital modeling. He is a member of Youth Committee of Risk and Insurance Research Branch of China Civil Engineering Society and a corresponding member of ISSMGE TC304 (Risk), TC309 (Machine Learning) and TC222 (Digital Twins). Jian Ji is a Jiangsu Specially-Appointed Professor of geotechnical engineering at Hohai University, Nanjing, China. He earned a PhD from the Nanyang Technological University of Singapore (NTU) in 2012. His research interests include numerical analysis of geotechnical problems with probabilistic considerations, slope stability and landslide mitigation, ground excavation and earth retaining systems, underground pipeline safety, etc. He received the 2018 Outstanding Paper Award from the Elsevier's international journal Computers and Geotechnics. He is an associate editor of Advances in Civil Engineering, editorial board member of Computers and Geotechnics, and member of ASCE. Peng Zeng received his PhD degree from the
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Technical University of Madrid (Madrid, Spain) in 2015. He is currently a professor at the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP), Chengdu University of Technology (CDUT). His main research interests include geotechnical reliability analysis and design, landslide risk assessment, tunnel squeezing and rockburst hazard prediction. He is the chief scientist for Everest Scientific Research Program of CDUT. Zijun Cao is currently a professor in the School of Water Resources and Hydropower Engineering, Wuhan University, China. He earned his PhD in geotechnical engineering from City University of Hong Kong in 2012. He is the assistant editor and an editorial board member of the journal of Georisk. His main research areas include probabilistic site characterization with particular interests in the quantification of uncertainties in soil properties, efficient probabilistic analysis, and risk assessment of slope stability, practical reliability based design of geotechnical structures.
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