Ground Shock Propagation in Spatially Random Geological Media

Abstract

Report developed under SBlR contract for topic HDTRA1-05-P-0026. The main issue being addressed is as follows: Given what is known about a rock site, what can one say about the uncertainty in the ground-shock environment that can be expected from an explosive event? To be sure, the amount of data available is directly connected with the ground-shock uncertainty. But does a quantitative, rational procedure exist that one can use to estimate the environment uncertainty associated to the degree of uncertainty on the site? We have demonstrated in this project: (1) How heterogeneity and spatial variability of rock properties at a site, as evidenced by state-of-the-art measurements taken at the demonstration site, are captured as random fields using geostatistical techniques; (2) How these fields of mostly low-stress, in-situ or laboratory properties, which are representative of the kinds of measurements being taken, could be used to infer rock-mass mechanical properties in the high-stress, high-strain-rate response regimes of interest; (3) How the rock-mass property fields were used in ground-shock simulations to generate the corresponding ground-shock fields, employing stochastic finite-element techniques; and, lastly, (4) How local variability in the simulated ground-shock environments could be readily elicited from the stochastic realizations.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2005
Accession Number
ADA441146

Entities

People

  • F. Wong
  • J. Mould
  • V. Pereyra

Tags

Communities of Interest

  • Counter IED
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Compressive Strength
  • Databases
  • Demonstrations
  • Engineering
  • Environment
  • Explosives
  • Ground Shock
  • Heterogeneity
  • Materials
  • Measurement
  • Mechanical Properties
  • Mechanics
  • Simulations
  • Standards
  • Strain Rate
  • Three Dimensional
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Acoustical Oceanography.
  • Computational Fluid Dynamics (CFD)
  • Geotechnical Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference