Development of an Artificial Neural Network for Real-Time Classification of Cone Penetrometer Strain Gauge Data.

Abstract

This document describes the development of an artificial neural-network-based algorithm for classifying soil behavior type from cone penetrometer strain gauge data. The network input consists of the two standard cone penetration test parameters: the logarithm of cone pressure and the percentage of sleeve friction to cone pressure (friction ratio). Network output is a one-of-n coding of 12 soil classifications. Three- and four-layer backpropagation networks are trained to associate 11,000 data points with the appropriate soil type. The best recall performance is obtained from a four-layer, 2 x 15 x 15 x 12 network with a tested accuracy rate of 98.2%. All classification errors occur at the decision boundaries between class regions. The network was incorporated into the data collection software of the prototype SCAPS vehicle in October 1993. The C source code is included as appendix A.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1994
Accession Number
ADA290719

Entities

People

  • John M. Andrews
  • Stephen H. Lieberman

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Application Software
  • Boundaries
  • Classification
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Cone Penetration Tests
  • Errors
  • Friction
  • Gages
  • Measurement
  • Neural Networks
  • Soil Classification
  • Standards
  • Strain Gages

Readers

  • Environmental Remediation and Restoration.
  • Mechanical Engineering/Mechanics of Materials.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks