Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Rezania:2010:CG,
-
author = "Mohammad Rezania and Akbar A. Javadi and
Orazio Giustolisi",
-
title = "Evaluation of liquefaction potential based on CPT
results using evolutionary polynomial regression",
-
journal = "Computers and Geotechnics",
-
year = "2010",
-
volume = "37",
-
number = "1-2",
-
pages = "82--92",
-
month = jan # "-" # mar,
-
keywords = "genetic algorithms, genetic programming, Geotechnical
models, Soil liquefaction, Earthquake, Evolutionary
computation",
-
ISSN = "0266-352X",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0266352X09001311",
-
DOI = "doi:10.1016/j.compgeo.2009.07.006",
-
size = "11 pages",
-
abstract = "In this paper a new approach is presented, based on
evolutionary polynomial regression (EPR), for
determination of liquefaction potential of sands. EPR
models are developed and validated using a database of
170 liquefaction and non-liquefaction field case
histories for sandy soils based on CPT results. Three
models are presented to relate liquefaction potential
to soil geometric and geotechnical parameters as well
as earthquake characteristics. It is shown that the EPR
model is able to learn, with a very high accuracy, the
complex relationship between liquefaction and its
contributing factors in the form of a function. The
attained function can then be used to generalise the
learning to predict liquefaction potential for new
cases not used in the construction of the model. The
results of the developed EPR models are compared with a
conventional model as well as a number of neural
network-based models. It is shown that the proposed EPR
model provides more accurate results than the
conventional model and the accuracy of the EPR results
is better than or at least comparable to that of the
neural network-based models proposed in the literature.
The advantages of the proposed EPR model over the
conventional and neural network-based models are
highlighted.",
- }
Genetic Programming entries for
Mohammad Rezania
Akbar A Javadi
Orazio Giustolisi
Citations