| Authors: |
A. Johari
and G. Habibagahi
and A. Ghahramani
|
| Tags: |
algorithms,
genetic
programming
|
| Abstract: |
In this technical note, a genetic programming (GP)
approach is employed to predict the soil-water
characteristic curve (SWCC) of soils. The GP model
requires an input terminal set that consists of initial
void ratio, initial gravimetric water content,
logarithm of suction normalised with respect to
atmospheric air pressure, clay content, and silt
content. The output terminal set consists of the
gravimetric water content corresponding to the assigned
input suction. The function set includes operators such
as plus, minus, product, division, and power. Results
from pressure plate tests carried out on clay, silty
clay, sandy loam, and loam compiled in the SoilVision
software were adopted as a database for developing and
validating the genetic model. For this purpose, and
after data digitisation, GP software (GPLAB) provided
by MATLAB was employed for the analysis. Furthermore,
GP simulations were compared with the experimental
results as well as the models proposed by other
investigators. This comparison indicated superior
performance of the proposed model for predicting the
SWCC. |
@article{Johari:2006:JGGE,
title = {Prediction of Soil-Water Characteristic Curve Using
Genetic Programming},
author = {A. Johari and G. Habibagahi and A. Ghahramani},
journal = {Journal of Geotechnical and Geoenvironmental
Engineering},
month = {May},
number = {5},
pages = {661--665},
volume = {132},
year = {2006},
abstract = {In this technical note, a genetic programming (GP)
approach is employed to predict the soil-water
characteristic curve (SWCC) of soils. The GP model
requires an input terminal set that consists of initial
void ratio, initial gravimetric water content,
logarithm of suction normalised with respect to
atmospheric air pressure, clay content, and silt
content. The output terminal set consists of the
gravimetric water content corresponding to the assigned
input suction. The function set includes operators such
as plus, minus, product, division, and power. Results
from pressure plate tests carried out on clay, silty
clay, sandy loam, and loam compiled in the SoilVision
software were adopted as a database for developing and
validating the genetic model. For this purpose, and
after data digitisation, GP software (GPLAB) provided
by MATLAB was employed for the analysis. Furthermore,
GP simulations were compared with the experimental
results as well as the models proposed by other
investigators. This comparison indicated superior
performance of the proposed model for predicting the
SWCC.},
notes = {c2006 ASCE}, doi = {doi:10.1061/(ASCE)1090-0241(2006)132:5(661)},
keywords = {algorithms, genetic programming }
}
::