An empirical model for shear capacity of RC deep beams using genetic-simulated annealing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Gandomi:2013:ACME,
-
author = "A. H. Gandomi and A. H. Alavi and
D. Mohammadzadeh Shadmehri and M. G. Sahab",
-
title = "An empirical model for shear capacity of {RC} deep
beams using genetic-simulated annealing",
-
journal = "Archives of Civil and Mechanical Engineering",
-
year = "2013",
-
volume = "13",
-
number = "3",
-
pages = "354--369",
-
keywords = "genetic algorithms, genetic programming, Shear
capacity, RC deep beam, Genetic-simulated annealing,
Empirical formula",
-
ISSN = "1644-9665",
-
DOI = "doi:10.1016/j.acme.2013.02.007",
-
URL = "http://www.sciencedirect.com/science/article/pii/S1644966513000319",
-
size = "16 pages",
-
abstract = "This paper presents an empirical model to predict the
shear strength of RC deep beams. A hybrid search
algorithm coupling genetic programming (GP) and
simulated annealing (SA), called genetic simulated
annealing (GSA), was used to develop mathematical
relationship between the experimental data. Using this
algorithm, a constitutive relationship was obtained to
make pertinent the shear strength of deep beams to nine
mechanical and geometrical parameters. The model was
developed using an experimental database acquired from
the literature. The results indicate that the proposed
empirical model is properly capable of evaluating the
shear strength of deep beams. The validity of the
proposed model was examined by comparing its results
with those obtained from American Concrete Institute
(ACI) and Canadian Standard Association (CSA) codes.
The derived equation is notably simple and includes
several effective parameters.",
- }
Genetic Programming entries for
A H Gandomi
A H Alavi
D Mohammadzadeh Shadmehri
Mohammad Ghasem Sahab
Citations