Accession Number : ADA290719
Title : Development of an Artificial Neural Network for Real-Time Classification of Cone Penetrometer Strain Gauge Data.
Descriptive Note : Final rept.,
Corporate Author : NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA
Personal Author(s) : Andrews, John M. ; Lieberman, Stephen H.
PDF Url : ADA290719
Report Date : OCT 1994
Pagination or Media Count : 29
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.
Descriptors : *COMPUTER PROGRAMS, *NEURAL NETS, *STRAIN GAGES, *SOIL CLASSIFICATION, INPUT, OUTPUT, SOURCES, RATIOS, DECISION MAKING, NETWORKS, REAL TIME, SOIL MECHANICS, REGIONS, BOUNDARIES, CODING, SOILS, ERRORS, PRESSURE, CLASSIFICATION, DATA ACQUISITION, LOGARITHM FUNCTIONS, FRICTION, CONICAL BODIES, SLEEVES, RECALL.
Subject Categories : Soil Mechanics
Computer Programming and Software
Distribution Statement : APPROVED FOR PUBLIC RELEASE