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

Auteur(s):








Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-23
DOI: 10.1155/2020/8850535
Abstrait:

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.

Copyright: © Fahid Aslam et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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  • Reference-ID
    10444057
  • Publié(e) le:
    05.10.2020
  • Modifié(e) le:
    02.06.2021