Application of the Optimized Regression to Volume Expansion Evaluation of Cement Paste with Fly Ash and MgO Expansive Additive
DOI:
https://doi.org/10.14311/Keywords:
Cement Paste, Expansion, Fly Ash, Expansive Additive, Extra Tree Regression, Morris Feature SelectionAbstract
Few studies have been conducted on the use of machine learning (ML) techniques to predict the volume expansion (Ve) of cement paste when fly ash (FA) and MgO expansive addition (MEA) are present. Utilizing a set of data that contained 170 experimental results that were obtained from previous research, the purpose of this study was to develop and evaluate ML algorithms for the assessment of Ve. To achieve this, the Extra tree regression (ETR) was created. The technique uses FA, Portland cement (PC), MEA, and sample age (Age) as input parameters. The Red-Tailed Hawk Algorithm (RTHA) and the Electric Eel Foraging Algorithm (EEFA) establish the hyperparameters of ETR, which greatly affect its effectiveness. The variation percentages of the two models developed for these measures are a minimum of 13%; in some cases, this variation decreases by 53%, illustrating the ETR (R)'s predictive reliability and efficacy. For instance, for the RMSE index, ETR (R) achieved values of 0.0053 and 0.01 during the training and testing phases, which are about 28% and 14% lower than the corresponding values of ETR (E) at 0.0068 and 0.0114, correspondingly. The ETR (R) model is somewhat superior to the alternative model regarding its purpose.
Received: 21.10.2025
Received in revised form: 13.2.2025
Accepted: 1.9.2025
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