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ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Construction Materials

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1558394

This article is part of the Research Topic Innovative Materials and Techniques for Sustainable Construction View all 3 articles

Chopped and Minibars reinforced High-Performance Concrete: Machine Learning Prediction of Mechanical Properties

Provisionally accepted
  • 1 Peoples' Friendship University of Russia, Moscow, Russia
  • 2 Peter the Great St.Petersburg Polytechnic University, Saint Petersburg, Saint Petersburg, Russia
  • 3 Moscow State University of Civil Engineering, Moscow, Moscow Oblast, Russia

The final, formatted version of the article will be published soon.

    A novel form of high-tech concrete known basalt fiber-reinforced high-performance concrete (BFHPC) has been developed using traditional materials that require extra admixtures to improve its mechanical properties. Machine learning (ML) techniques provide a more flexible and economical way to predict the mechanical property of chopped and minibar basalt fiber-reinforced high-performance concrete based on material properties and processing parameters, enabling durable and environmentally friendly construction. Predicting the mechanical properties of BFHPC precisely is crucial since it reduces design costs and time, and it also minimizes material waste from several mixing experiments. In this study, the compressive strength and flexural strength are predicted via different types of machine learning models. Experiments carried out in the laboratory under standard controlled settings at 7, 14, and 28-day curing periods yielded sample data for analysis and model development. The mechanical characteristics of BFHPC have been predicted using a combination of decision tree, partial least squares, lasso, rigid, random forest regressor, K Neighbours, and linear regressions. According to the results, all types of regression have the best results except KNeighbors Regressor, Random Forest Regressor, and Lasso Regression, with a correlation coefficient of R 2 93%. Each model's performance and application are examined thoroughly, leading to the identification of useful suggestions, existing knowledge gaps, and areas in need of further study.

    Keywords: Data Mining, dynamic analysis, Reinforced concrete beam, prediction, Basalt fiber

    Received: 10 Jan 2025; Accepted: 27 Mar 2025.

    Copyright: © 2025 Gebre, Vatin and Hematibahar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Tesfaldet Hadgembes Gebre, Peoples' Friendship University of Russia, Moscow, Russia
    Nikolai Ivanovich Vatin, Peoples' Friendship University of Russia, Moscow, Russia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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