Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete

The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modellin...

Full description

Saved in:
Bibliographic Details
Published inAdvances in Civil Engineering Vol. 2020; no. 2020; pp. 1 - 23
Main Authors Alabdulijabbar, Hisham, Alyousef, Rayed, Akbar, Arslan, Waheed, Abdul, Khan, Kaffayatullah, Amin, Muhammad Nasir, Farooq, Furqan, Aslam, Fahid, Javed, Muhammad Faisal
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Accuracy
Algorithms
Artificial intelligence
Cement
Civil engineering
Compressive strength
Concrete construction
Design of experiments
Design specifications
Empirical analysis
Error analysis
Gene expression
Genetic engineering
High strength concretes
Machine learning
Mechanical properties
Regression analysis
Regression models
Statistical analysis
Superplasticizers
Online AccessGet full text

Cover

More Information
Summary:The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.
ISSN:1687-8086
1687-8094
DOI:10.1155/2020/8850535