Editors: Samui, Pijush, Tien Bui, Dieu and Chakraborty, Subrata et.al

Publication Year: 2020

Publisher: Elsevier Science & Technology

ISBN: 978-0-12-816514-0

Category: Technology & Engineering - Engineering

Image Count:
195

Book Status:
Pending

Predicted Release Month:

Table of Contents

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields.

- Contributors
- Chapter 1: Fundamentals of reliability analysis
- Acknowledgments
- 1: Introduction
- 2: Important steps in reliability evaluation
- 3: Elements of set theory
- 4: Quantification of uncertainties in random variables
- 5: Transformation of uncertainty from parameter to the system level
- 6: Fundamentals of reliability analysis
- 7: Performance-based seismic design
- 8: Monte Carlo simulation
- 9: Alternative to simulations
- 10: Computer programs
- 11: Education
- Chapter 2: Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression
- Acknowledgments
- 1: Introduction
- 2: Theoretical framework
- 3: Materials and method
- 4: Results
- 5: Discussion: limitations and opportunity for further research
- 6: Conclusion
- Chapter 3: Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas
- Acknowledgments
- 3.1: Introduction
- 3.2: Theoretical framework
- 3.3: Materials and method
- 3.4: Results
- 3.5: Discussion: limitations and opportunity for further research
- 3.6: Conclusion
- Chapter 4: A model for quantitative fire risk assessment integrating agent-based model with automatic event tree analysis
- 1: Introduction
- 2: Methodology
- 3: Case study
- 4: Results and discussion
- 5: Conclusions
- Chapter 5: Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates
- 1: Introduction
- 2: Governing equations
- 3: Polynomial neural network
- 4: Results and discussion
- 5: Conclusion
- Chapter 6: Development of copula-statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index
- 1: Introduction
- 2: Materials and methods
- 3: Results and discussion
- 4: Further discussion
- 5: Summary
- Chapter 7: An efficient approximation-based robust design optimization framework for large-scale structural systems
- 1: Introduction
- 2: Surrogate-assisted robust design optimization
- 3: Proposed surrogate-assisted robust design optimization framework
- 4: Proposed surrogate models
- 5: Numerical validation
- 6: Summary and conclusions
- Appendix A. Description of the test problems investigated in Section 5.1
- Chapter 8: Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression
- Acknowledgments
- 1: Introduction
- 2: Data and methodology
- 3: Results
- 4: Discussion
- 5: Conclusions
- Chapter 9: Geostatistics: principles and methods
- 1: Introduction
- 2: Difference between geostatistics and classical statistics methods
- 3: Regionalized variables
- 4: Random variable
- 5: Variogram and semivariogram
- 6: Variogram specifications
- 7: Range
- 8: Sill
- 9: Nugget
- 10: Model fitting to empirical variogram
- 11: Models with sill
- 12: Spherical model
- 13: Exponential model
- 14: Gaussian model
- 15: Models without sill
- 16: Linear model
- 17: Parabolic model
- 18: The DeWijsian model
- 19: Selection of the theoretical variogram models
- 20: Locative continuity analysis of variogram
- 21: Anisotropy in a variogram
- 22: Geometric anisotropy
- 23: Zonal anisotropy
- 24: Geostatistics methods
- 25: Kriging Interpolation method
- 26: Kriging equations
- 27: Kriging types
- 28: Ordinary Kriging
- 29: Simple Kriging
- 30: Lognormal Kriging
- 31: Universal Kriging
- 32: Indicator Kriging
- 33: Disjunctive Kriging
- 34: Co-Kriging
- 35: Kriging parameters
- 36: Weights
- 37: Neighborhood
- 38: Search radius
- 39: Conclusions
- Chapter 10: Adaptive H∞ Kalman filtering for stochastic systems with nonlinear uncertainties
- Acknowledgments
- 1: Introduction
- 2: Problem: H∞ state estimation
- 3: Approach: adaptive H∞ filtering
- 4: Example: maneuvering target tracking in space
- 5: Numerical simulations
- 6: Conclusions
- 7: Appendix
- Chapter 11: R for lifetime data modeling via probability distributions
- 1: Introduction
- 2: R installations, help and advantages
- 3: Operators in R
- 4: Programming with user-defined functions
- 5: Loops and if/else statements in R
- 6: Curve plotting in R
- 7: Maximum likelihood estimation
- 8: Lifetime data modeling
- 9: Conclusion
- Chapter 12: Probability-based approach for evaluating groundwater risk assessment in Sina basin, India
- 1: Introduction
- 2: Study area
- 3: Methodology
- 4: Results and discussion
- 5: Conclusions
- Chapter 13: Novel concepts for reliability analysis of dynamic structural systems
- Acknowledgments
- 1: Introduction
- 2: Challenges and trends in risk evaluation
- 3: State-of-the-art in estimating risk of dynamic structural systems
- 4: A novel structural risk estimation procedure for dynamic loadings applied in time domain
- 5: Proposed novel concept for reliability analysis of dynamic structural systems
- 6: Accuracy in generating an IRS
- 7: Reliability estimation
- 8: Numerical examples—verifications and case studies
- 9: Multidisciplinary applications
- 10: Further improvements of Kriging method
- 11: Conclusions
- Chapter 14: Probabilistic neural networks: a brief overview of theory, implementation, and application
- 1: Introduction
- 2: Preliminary concepts: nonparametric estimation methods
- 3: Structure of probabilistic neural networks
- 4: Improving memory performance
- 5: Simple probabilistic neural network example in Python
- 6: Conclusions
- Chapter 15: Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels
- Chapter points
- 1: Introduction
- 2: Polynomial chaos expansions
- 3: Design of experiments
- 4: Examples
- 5: Summary
- Chapter 16: Stochastic response of primary–secondary coupled systems under uncertain ground excitation using generalized polynomial chaos method
- 1: Introduction
- 2: Details of the generalized polynomial chaos method
- 3: Deterministic model of base-isolated SDOF and base-isolated MDOF structure with secondary system
- 4: Stochastic structural dynamics using gPC method
- 5: Numerical study
- 6: Conclusions
- Chapter 17: Stochastic optimization stochastic diffusion search algorithm
- 1: Introduction
- 2: Stochastic diffusion search
- 3: Search and optimization
- 4: Markov chain model
- 5: Convergence of the stochastic diffusion search
- 6: Time complexity
- 7: Conclusions
- Chapter 18: Resampling methods combined with Rao-Blackwellized Monte Carlo Data Association algorithm
- 1: Introduction
- 2: Rao-Blackwellized Monte Carlo Data Association
- 3: Resampling techniques
- 4: Experiments and simulation results
- 5: Conclusions
- Chapter 19: Back-propagation neural network modeling on the load–settlement response of single piles
- Acknowledgments
- 1: Introduction
- 2: Back-propagation neural network methodologies
- 3: Development of the back-propagation neural network model
- 4: The optimal back-propagation neural network model
- 5: Modeling results
- 6: Parametric relative importance
- 7: Model interpretabilities
- 8: Summary and conclusion
- Appendix A BPNN pile settlement model
- Appendix B weights and bias values for BPNN pile settlement model
- Chapter 20: A Monte Carlo approach applied to sensitivity analysis of criteria impacts on solar PV site selection
- 1: Introduction
- 2: Literature review
- 3: Sensitivity analysis using Monte Carlo simulation
- 4: Conclusion
- Chapter 21: Stochastic analysis basics and application of statistical linearization technique on a controlled system with nonlinear viscous dampers
- 1: Introduction
- 2: Power spectral density
- 3: Input–output relationship
- 4: Monte Carlo simulation
- 5: Fluid viscous dampers
- 6: Conclusion
- Chapter 22: A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates
- Acknowledgments
- 1: Introduction
- 2: Governing equations for composite plates
- 3: Artificial neural network
- 4: Polynomial neural network
- 5: Stochastic approach using neural network model
- 6: Results and discussion
- 7: Summary