Journal of Applied Science and Engineering

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Weiying Wu1This email address is being protected from spambots. You need JavaScript enabled to view it. and Hui Huang2

1Xinyang vocational and technical college; Xinyang Henan, 464000, China

2Xinyang highway development center; Xinyang Henan, 464000, China

 

 

Received: January 16, 2024
Accepted: May 19, 2025
Publication Date: July 11, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202603_29(3).0020  


Carbon emissions from Portland cement production can be reduced by adding groundgranulated blast furnace slag (GGBFS). fc is essential in the concrete mixes and construction of concrete forms. This work intends to present a sound methodology for the in-depth review of ML tactics to have better predictions of fc of concrete, including GGBFS applications. The research will propose schemes to anticipate the values of fc by using Coupled Random Forests (RF) analysis. The value of fc for the datasets collected in this research varies from 6.3 to 101.3 MPa. The investigation employed the Honey badger optimization (HBO) and Chimp optimization algorithm (COA) to enhance the efficacy of the RF approaches. This analysis will be significant in many ways, as this involves the use of COA and HBO tactics, GGBFS for estimating fc, comparison with previous research data and a full set of datasets with variation in input parameters. The anticipation of the mechanical traits of concrete employing such a method will give more accuracy and will improve the productivity of the anticipation pattern. The outcomes indicated that the merged RFHBO and RFCOA systems were able to estimate. R2 values for RFHBOare0.9961 in the training step, 0.9975 in the validation step, and 0.9971 in the test step. These results not only endorse the advancement of sustainable building materials but also establish a new standard for predictive modeling in concrete performance evaluation, rendering the approach beneficial for both scholarly research and practical engineering applications.

 


Keywords: Concrete; Ground granulated blast furnace slag; Simulation; Random forests; Honey badger optimization


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