Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{ALASKAR:2023:cscm,
-
author = "Abdulaziz Alaskar and Ghasan Alfalah and
Fadi Althoey and Mohammed Awad Abuhussain and
Muhammad Faisal Javed and Ahmed Farouk Deifalla and Nivin A. Ghamry",
-
title = "Comparative study of genetic programming-based
algorithms for predicting the compressive strength of
concrete at elevated temperature",
-
journal = "Case Studies in Construction Materials",
-
volume = "18",
-
pages = "e02199",
-
year = "2023",
-
keywords = "genetic algorithms, genetic programming, gene
expression programming",
-
ISSN = "2214-5095",
-
DOI = "doi:10.1016/j.cscm.2023.e02199",
-
URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003790",
-
abstract = "The elevated temperature severely influences the mixed
properties of concrete, causing a decrease in its
strength properties. Accurate proportioning of concrete
components for obtaining the required compressive
strength (C-S) at elevated temperatures is a
complicated and time-taking process. However, using
evolutionary programming techniques such as gene
expression programming (GEP) and multi-expression
programming (MEP) provides the accurate prediction of
concrete C-S. This article presents the genetic
programming-based models (such as gene expression
programming (GEP) and multi-expression programming
(MEP)) for forecasting the concrete compressive
strength (C-S) at elevated temperatures. In this
regard, 207 C-S values at elevated temperatures were
obtained from previous studies. In the model's
development, C-S was considered as the output parameter
with the nine most influential input parameters,
including; Nano silica, cement, fly ash, water,
temperature, silica fume, superplasticizer, sand, and
gravels. The efficacy and accuracy of the GEP and
MEP-based models were assessed by using statistical
measures such as mean absolute error (MAE), correlation
coefficient (R2), and root mean square error (RMSE).
Moreover, models were also evaluated for external
validation using different validation criteria
recommended by previous studies. In comparing GEP and
MEP models, GEP gave higher R2 and lower RMSE and MAE
values of 0.854, 5.331 MPa, and 0.018 MPa respectively,
indicating a strong correlation between actual and
anticipated outputs. Thus, the GEP-based model was used
further for sensitivity analysis, which revealed that
cement is the most influencing factor. In addition, the
proposed GEP model provides simple mathematical
expression that can be easily implemented in practice",
- }
Genetic Programming entries for
Abdulaziz Alaskar
Ghasan Alfalah
Fadi Althoey
Mohammed Awad Abuhussain
Muhammad Faisal Javed
Ahmed Farouk Mohamed Hassan Deifalla
Nivin A Ghamry
Citations