Tree Based Approaches for Predicting Concrete Carbonation Coefficient
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{londhe:2022:AS,
-
author = "Shreenivas Londhe and Preeti Kulkarni and
Pradnya Dixit and Ana Silva and Rui Neves and
Jorge {de Brito}",
-
title = "Tree Based Approaches for Predicting Concrete
Carbonation Coefficient",
-
journal = "Applied Sciences",
-
year = "2022",
-
volume = "12",
-
number = "8",
-
pages = "Article No. 3874",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2076-3417",
-
URL = "https://www.mdpi.com/2076-3417/12/8/3874",
-
DOI = "doi:10.3390/app12083874",
-
abstract = "Carbonation is one of the critical durability issues
in reinforced concrete structures in terms of their
structural integrity and safety and may cause the fatal
deterioration and corrosion of steel reinforcement if
ignored. Many researchers have performed a considerable
number of studies to predict the carbonation of
concrete structures. However, it is still challenging
to predict the carbonation depth or carbonation
coefficient, as they depend on various factors.
Therefore, creating a model that can learn from
available data using Data Driven Techniques (DDT) is a
step forward in this research field. This study
provides new approaches to predict the carbonation
coefficient of concrete through Model Tree (MT), Random
Forest (RF) and Multi-Gene Genetic Programming (MGGP)
approaches. With 827 case studies, the predicted models
can be seen as a function of a set of conditioning
factors, which are statistically significant in
explaining the carbonation mechanism. The results
obtained through MT, RF and MGGP were compared with
those obtained through Multiple Linear Regression
(MLR), Artificial Neural Networks (ANNs) and Genetic
Programming (which were previously developed). The
results reveal that the MT, RF and MGGP perform better
than the previous models. Moreover, the MT technique
displays its output in terms of series of equations, RF
as multiple trees and MGGP in form of a single
equation, which are more user-friendly and applicable
in practice.",
-
notes = "also known as \cite{app12083874}",
- }
Genetic Programming entries for
S N Londhe
Preeti S Kulkarni
Pradnya R Dixit
Ana Silva
Rui Neves Madeira
Jorge Manuel Calico Lopes de Brito
Citations