Probabilistic Slope Stability Analysis with the

Slides:

Advertisements

Similar presentations

Copula Representation of Joint Risk Driver Distribution

Advertisements

Structural reliability analysis with probability- boxes Hao Zhang School of Civil Engineering, University of Sydney, NSW 2006, Australia Michael Beer Institute.

1 Overview of Simulation When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions set for analytic.

11 Simulation. 22 Overview of Simulation – When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions.

Monte Carlo Simulation A technique that helps modelers examine the consequences of continuous risk Most risks in real world generate hundreds of possible.

Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.

EPI 809/Spring Probability Distribution of Random Error.

Training Manual Aug Probabilistic Design: Bringing FEA closer to REALITY! 2.5 Probabilistic Design Exploring randomness and scatter.

Simple Linear Regression

Probability Distributions and Stochastic Budgeting AEC 851 – Agribusiness Operations Management Spring, 2006.

May 2007 Hua Fan University, Taipei An Introductory Talk on Reliability Analysis With contribution from Yung Chia HSU Jeen-Shang Lin University of Pittsburgh.

GoldSim 2006 User Conference Slide 1 Vancouver, B.C. The Submodel Element.

1 Risk in the MOLINO André de Palma UCP & Ecole Nationale des Ponts et Chaussée Lætitia Andrieu CERMICS-ENPC Nathalie Picard University of Cergy-Pontoise.

Understanding the Accuracy of Assembly Variation Analysis Methods ADCATS 2000 Robert Cvetko June 2000.

Simulation Modeling and Analysis

Contemporary Engineering Economics, 4 th edition, © 2007 Risk Simulation Lecture No. 49 Chapter 12 Contemporary Engineering Economics Copyright, © 2006.

Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.

Uncertainty in Engineering - Introduction Jake Blanchard Fall 2010 Uncertainty Analysis for Engineers1.

Decision analysis and Risk Management course in Kuopio

46th SIAA Structures, Dynamics and Materials Conference Austin, TX, April 18-21, 2005 Application of the Generalized Conditional Expectation Method for.

1 D r a f t Life Cycle Assessment A product-oriented method for sustainability analysis UNEP LCA Training Kit Module k – Uncertainty in LCA.

Delivering Integrated, Sustainable, Water Resources Solutions Monte Carlo Simulation Robert C. Patev North Atlantic Division – Regional Technical.

Introduction to ModelingMonte Carlo Simulation Expensive Not always practical Time consuming Impossible for all situations Can be complex Cons Pros Experience.

Monte Carlo Simulation 1.  Simulations where random values are used but the explicit passage of time is not modeled Static simulation  Introduction.

Computer Simulation A Laboratory to Evaluate “What-if” Questions.

Quantify prediction uncertainty (Book, p ) Prediction standard deviations (Book, p. 180): A measure of prediction uncertainty Calculated by translating.

© Harry Campbell & Richard Brown School of Economics The University of Queensland BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets.

Gaussian process modelling

Monte Carlo Simulation CWR 6536 Stochastic Subsurface Hydrology.

General Principle of Monte Carlo Fall 2013 By Yaohang Li, Ph.D.

Presentation Ling Zhang Date: Framework of the method 1 Using Distribution Fitting for Assumptions 2 monte – carlo simulation 3 compare different.

Chapter 14 Monte Carlo Simulation Introduction Find several parameters Parameter follow the specific probability distribution Generate parameter.

Probabilistic Mechanism Analysis. Outline Uncertainty in mechanisms Why consider uncertainty Basics of uncertainty Probabilistic mechanism analysis Examples.

Chapter 10 Introduction to Simulation Modeling Monte Carlo Simulation.

IIASA Yuri Ermoliev International Institute for Applied Systems Analysis Mathematical methods for robust solutions.

Contemporary Engineering Economics, 6 th edition Park Copyright © 2016 by Pearson Education, Inc. All Rights Reserved Risk Simulation Lecture No. 40 Chapter.

The Common Shock Model for Correlations Between Lines of Insurance

1 6. Reliability computations Objectives Learn how to compute reliability of a component given the probability distributions on the stress,S, and the strength,

1 Chapter 19 Monte Carlo Valuation. 2 Simulation of future stock prices and using these simulated prices to compute the discounted expected payoff of.

MBA7025_01.ppt/Jan 13, 2015/Page 1 Georgia State University - Confidential MBA 7025 Statistical Business Analysis Introduction - Why Business Analysis.

MGS3100_01.ppt/Aug 25, 2015/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Introduction - Why Business Analysis Aug 25 and 26,

Outline of Chapter 9: Using Simulation to Solve Decision Problems Real world decisions are often too complex to be analyzed effectively using influence.

Simulation is the process of studying the behavior of a real system by using a model that replicates the behavior of the system under different scenarios.

MBA7020_01.ppt/June 13, 2005/Page 1 Georgia State University - Confidential MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis.

Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.

5-1 ANSYS, Inc. Proprietary © 2009 ANSYS, Inc. All rights reserved. May 28, 2009 Inventory # Chapter 5 Six Sigma.

FIN 614: Financial Management Larry Schrenk, Instructor.

Monte Carlo Process Risk Analysis for Water Resources Planning and Management Institute for Water Resources 2008.

GoldSim Technology Group LLC, 2006 Slide 1 Sensitivity and Uncertainty Analysis and Optimization in GoldSim.

Machine Design Under Uncertainty. Outline Uncertainty in mechanical components Why consider uncertainty Basics of uncertainty Uncertainty analysis for.

1 Economics 331b Treatment of Uncertainty in Economics (I)

Monte-Carlo Simulations Seminar Project. Task  To Build an application in Excel/VBA to solve option prices.  Use a stochastic volatility in the model.

Reservoir Uncertainty Assessment Using Machine Learning Techniques Authors: Jincong He Department of Energy Resources Engineering AbstractIntroduction.

Probabilistic Design Systems (PDS) Chapter Seven.

1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.

NESSUS Overview and General Capabilities

Uncertainties in fluid-structure interaction simulations

Reducing Uncertainty in Fatigue Life Estimates Design, Analysis, and Simulation 1-77-Nastran A Probabilistic Approach To Modeling Fatigue.

MAT 4830 Mathematical Modeling 04 Monte Carlo Integrations

System Analysis System – set of interdependent elements that interact in order to accomplish a one or more final outcomes. Constrained and affected by:

HMP Simulation - Introduction Deterministic vs. Stochastic Models Risk Analysis Random Variables Best Case/Worst Case Analysis What-If Analysis.

Spreadsheet Applications for Construction Cost Estimating BY CHARLES NICKEL, P.E. (225)

1 Life Cycle Assessment A product-oriented method for sustainability analysis UNEP LCA Training Kit Module k – Uncertainty in LCA.

Supplementary Chapter B Optimization Models with Uncertainty

MEGN 537 – Probabilistic Biomechanics Ch

Monte Carlo Simulation

Monte Carlo Simulation Managing uncertainty in complex environments.

MEGN 537 – Probabilistic Biomechanics Ch

Simple Linear Regression

Presentation transcript:

Probabilistic Slope Stability Analysis with the “Response Surface Methodology” (Henry T. Chiwaye)

Scope of Presentation Overview of Response Surface Methodology (RSM) Implementation of RSM in probabilistic slope stability analysis Verification Examples General guidelines for use of RSM

Slope Design Approaches Deterministic Factor of Safety (FOS) Probabilistic Probability of Failure(POF) Risk Analysis Economic / Safety impact Uncertainty: Geology Strength Water

Probabilistic Analysis Monte Carlo Simulation Point Estimate Method (PEM)

Monte Carlo Simulation CC Friction angle Cohesion Frequency INPUT DATA MONTE CARLO ANALYSIS (SLIDE, Phase2, FLAC, UDEC ) Model OUTPUT RESULTS 1.0 Frequency FOS POF model = P ( FOS < 1.00 ) POF Highlights Large no. of runs (103). Reveals Sensitivities Very Flexible

Point Estimate Method (PEM) CC Friction angle Cohesion Frequency INPUT DATA 2n MODEL RUNS (SLIDE, Phase2, FLAC, UDEC ) Model OUTPUT RESULTS FOS Statistics Mean Variance POF Highlights Evaluate model at 2n points. Assume a form for the FOS probability distribution No sensitivity information

Response Surface Methodology Response Surface Techniques Monte Carlo Simulation Probability Of Failure (%)

Response Surface Techniques

Response Surface Techniques Concept Var 1 Var 2 FOS Evaluate model at selected points Use interpolation scheme to generate response surface

Response Surface Generation RSM Overview Response Surface Generation Model (SLIDE, UDEC etc) MONTE CARLO ANALYSIS EXCEL 1.0 POF OUTPUT RESULTS FOS Frequency

RSM Verification Approach RSM vs. Model (SLIDE) Cohesion & friction angle uncertain variables RSM using linear interpolation Models 90m

Model vs. RSM (POF %) Homogeneous Slope: (Normal)

Model vs. RSM (POF %) Homogeneous Slope : (Lognormal)

Model vs. RSM (POF %) 3 Material Slope : (Normal)

RSM vs. PEM (POF %) Requires 2n + 1 points vs. 2n for PEM

RSM Guidelines Piecewise Linear / Quadratic interpolation can be used. Grouping Variables Strength Cohesion Friction Angle Evaluation points must be in region of interest (+ / - 1 std dev).

Remarks Use of RSM with strongly correlated variables

Conclusions Good agreement between RSM and Monte Carlo Simulation Low computational times Practical way to incorporate numerical analysis in probabilistic slope design Reveals Sensitivities Very flexible

Questions?