文档介绍：Evolutionary data division methods for developing artificial work
models in geotechnical engineering
by
M A Shahin
H R Maier
M B Jaksa
Department of Civil & Environmental Engineering
The University of Adelaide
Research Report No. R 171
November, 2000
i
ABSTRACT
In recent years, artificial works (ANNs) have been applied to many
geotechnical engineering problems and have demonstrated some degree of ess.
In the majority of these applications, data division is carried out on an arbitrary
basis. However, the way the data are divided can have a significant effect on model
performance. In this report, the relationship between the statistical properties of
training, testing and validation sets and model performance and the effect of the
proportion of data used for training, testing and validation on model performance are
investigated for the case study of predicting the settlement of shallow foundations
on cohesionless soils. In addition, a novel approach for data division, which is
based on a anising map, is introduced and evaluated for the above case
study. The results obtained indicate that the statistical properties of the data in the
training, testing and validation sets need to be taken into account to ensure that
optimal model performance is achieved. The data division method introduced in
this paper is found to negate the need to choose which proportion of the data to use
for training, testing and validation and to ensure that each of the subsets are
representative of the available data.
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TABLE OF CONTENTS
ABSTRACT..................................................................................................... i
TABLE OF CONTENTS ................................................................................. ii
LIST OF FIGURES.......................................................................................... iii
LIST OF TABLES ..................................................................................