Application of gene expression programming to predict the compressive strength of quaternary-blended concrete

被引：6

作者：

Raheel M. ^{[1
,2
]}

Iqbal M. ^{[2
]}

Khan R. ^{[2
]}

Alam M. ^{[1
]}

Azab M. ^{[3
]}

Eldin S.M. ^{[4
]}

机构：

[1] Department of Civil Engineering, University of Engineering and Technology, Mardan

[2] Department of Civil Engineering, University of Engineering and Technology, Peshawar

[3] College of Engineering and Technology, American University of the Middle East, Egaila

[4] Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo

来源：

Asian Journal of Civil Engineering | 2023年 / 24卷 / 5期

关键词：

Compressive strength; Gene expression programming; Machine learning; Parametric and sensitivity analyses; Quaternary-blended concrete;

D O I：

10.1007/s42107-023-00573-w

中图分类号：

学科分类号：

摘要：

The potential impact of global warming is influencing life on planet earth. Besides other sources, production of cement is one of the major contributors of greenhouse gas emissions. Considering this, a study was undertaken to understand the influence of potential waste materials, which can replace cement and at the same time, make it economical and environment friendly. For this purpose, the influence of a quaternary blend of binders i.e., cement + 3 different pozzolanic materials on the compressive strength of concrete. Different variables such as the quantity of different binders, fine and coarse aggregate, water, superplasticizer and age of the samples were considered to study their influence on the compressive strength on the quaternary-blended concrete using gene expression programming (GEP). The number of chromosomes, genes and, the head size of GEP model was varied to study their influence on the predicted values of compressive strength. The performance of these different GEP models was also assessed using R2, RMSE and comparison of regression slopes. It was observed that the model with the number of chromosomes = 150, head size = 9 and the number of genes = 3, resulted in an optimum GEP model as apparent from its high R2 = 0.801 in the TR phase, and 0.800 in the TS phase, respectively. The regression slope analysis revealed that the forecasted values show good agreement with the actual values as evident from their higher R2 values (≥ 0.80). Similarly, monotonicity analysis for the best performing model M6 revealed that the addition of pozzolanic materials enhanced the compressive strength of quaternary-blended concrete. It was also observed that the compressive strength of quaternary-blended concrete increased sharply within the first 28 days of casting, thus validating the practical mechanics of concrete strength gaining process. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

引用

页码：1351 / 1364

页数：13

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