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CORRECTION article

Front. Mater., 20 October 2025

Sec. Structural Materials

Volume 12 - 2025 | https://doi.org/10.3389/fmats.2025.1721158

Correction: Prediction of compressive strength of high-performance concrete based on multiple machine learning models

This article is a correction to:

  1. Prediction of compressive strength of high-performance concrete based on multiple machine learning models

    1. Read original article
Kouchen Xiao Kouchen Xiao1*Hongjian ZhangHongjian Zhang2Sijia WeiSijia Wei2Chuanxin ZhuChuanxin Zhu2Jingtong HeJingtong He2Shuai ZhuShuai Zhu2Xiaohan YangXiaohan Yang2
  • 1Architectural and Civil Engineering, Jinken College of Technology, Nanjing, Jiangsu, China
  • 2College of Civil Engineering and Architecture, Xinjiang University, Urumqi, Xinjiang, China

A Correction on
Prediction of compressive strength of high-performance concrete based on multiple machine learning models

by Xiao K, Zhang H, Wei S, Zhu C, He J, Zhu S and Yang X (2025). Front. Mater. 12:1698248. doi: 10.3389/fmats.2025.1698248

An incorrect number was provided for Youth Project of China State Construction Engineering Corporation. The correct number is CSCEC-2024-Q-75.

The original article has been updated.

Publisher’s note

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.

Keywords: high-performance concrete, compressive strength, individual learner, ensemble learner, k-fold cross-validation

Citation: Xiao K, Zhang H, Wei S, Zhu C, He J, Zhu S and Yang X (2025) Correction: Prediction of compressive strength of high-performance concrete based on multiple machine learning models. Front. Mater. 12:1721158. doi: 10.3389/fmats.2025.1721158

Received: 09 October 2025; Accepted: 10 October 2025;
Published: 20 October 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Xiao, Zhang, Wei, Zhu, He, Zhu and Yang. 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) and the copyright owner(s) 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: Kouchen Xiao, eGlhb2tvdWNoZW5famtjdEAxNjMuY29t

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.