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ORIGINAL RESEARCH article

Front. Mater.
Sec. Structural Materials
Volume 11 - 2024 | doi: 10.3389/fmats.2024.1490006
This article is part of the Research Topic Structural Applications of Concrete with Recycled Solid Wastes and Alternatives for Cement View all 5 articles

A machine learning model for predicting the mechanical strength of cement-based materials filled with waste rubber modified by PVA

Provisionally accepted
He Zhengfeng He Zhengfeng 1Wu Zhuofan Wu Zhuofan 2*Niu Wenjun Niu Wenjun 2*Wang Fengcai Wang Fengcai 1*Zhong Shunjie Zhong Shunjie 3*Han Zeyu Han Zeyu 1*Zhao Qingxin Zhao Qingxin 4*
  • 1 Cangzhou Qugang Expressway Construction Co., LTD, Hebei, China
  • 2 School of Civil Engineering, Tianjin University, Tianjin, China
  • 3 Fujian Zhanglong Construction Investment Group Co., LTD, Fujian, 363101, China, Fujian, China
  • 4 School of Civil Engineering and Mechanics, Yanshan University, Hebei, China

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

    To further expand the application of waste rubber in civil engineering, this study proposes a method for modifying waste rubber with polyvinyl alcohol (PVA). A machine learning database was established based on the mechanical strength of cement-based materials filled with PVA-modified waste rubber. Various machine learning methods were employed to develop regression prediction models, compare their accuracy, and analyze the robustness of different influencing factors on strength indicators. Specifically, the Support Vector Regression (SVR) model demonstrated superior prediction performance, with a mean squared error (MSE) of 1.21 and 0.33, and a mean absolute error (MAE) of 2.06 and 0.15. The analysis revealed that rubber content and w/c ratio were negatively correlated with strength indicators, while curing age and PVA showed a positive correlation. Among all influencing factors, rubber content had the most significant impact on strength. Additionally, the results indicated that PVA-modified waste rubber significantly improved the mechanical strength of cement-based materials, likely due to the dispersing and bridging effects of PVA. This method not only enhanced material performance but also helped reduce the environmental burden of waste rubber, offering significant economic and environmental benefits.

    Keywords: Waste rubber, PVA, mechanical strength, machine learning, robustness

    Received: 02 Sep 2024; Accepted: 19 Sep 2024.

    Copyright: © 2024 Zhengfeng, Zhuofan, Wenjun, Fengcai, Shunjie, Zeyu and Qingxin. 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:
    Wu Zhuofan, School of Civil Engineering, Tianjin University, Tianjin, 300350, China
    Niu Wenjun, School of Civil Engineering, Tianjin University, Tianjin, 300350, China
    Wang Fengcai, Cangzhou Qugang Expressway Construction Co., LTD, Hebei, China
    Zhong Shunjie, Fujian Zhanglong Construction Investment Group Co., LTD, Fujian, 363101, China, Fujian, China
    Han Zeyu, Cangzhou Qugang Expressway Construction Co., LTD, Hebei, China
    Zhao Qingxin, School of Civil Engineering and Mechanics, Yanshan University, Hebei, China

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