Probabilistic Structural Analysis of Deep Tunnels.

Abstract

Accurate and efficient methods for performing probabilistic structural analysis are developed and demonstrated using highly detailed numerical tunnel models. Typical result from the probabilistic analysis include probability of failure, cumulative distribution functions, and probabilistic sensitivities with respect to all input statistical parameters. The results are useful for survivability/vulnerability assessments, strategic planning, tunnel design, and test planning. The report describes a program comprising development and application of several advanced probabilistic analysis methods, verification and validation of deterministic numerical models, and application of probabilistic analysis methods to several different tunnel problems. Statistical properties measured in laboratory testing are used directly in the calculations. A novel probabilistic cap constitutive model is developed that allows the model parameters to be treated as non-normal dependent random variables. The probabilistic methods used are based on a class of methods known as Fast Probability Integration (FPI). For probabilistic finite element analysis, the Advanced Mean Value (AMV) method is generally recommended. The methods are verified using Latin hypercube, adaptive importance sampling, and Monte Carlo Simulation. (MM)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1996
Accession Number
ADA304692

Entities

People

  • Ben H. Thacker
  • David S. Riha
  • Y. T. Wu

Organizations

  • Southwest Research Institute

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Data Science
  • Distribution Functions
  • Engineering
  • Geometry
  • Information Science
  • Mechanics
  • Modulus Of Elasticity
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Random Variables
  • Reliability
  • Sampling
  • Stress Strain Relations
  • Stress Waves
  • Structural Analysis

Readers

  • Computational Modeling and Simulation
  • Statistical inference.
  • Structural Health Monitoring of Composite Structures.