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Open Access

Expression of Concern

Expression of Concern: Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning

  • The PLOS One Editors

After this article [1] was published, the PLOS One Editors identified concerns about the article’s peer review. We regret that the issues were not addressed prior to the article’s publication. Readers are advised to interpret the article [1] with caution.

Reference

  1. 1. Jiang P, Zhao D, Jin C, Ye S, Luan C, Tufail RF. Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning. PLoS One. 2024;19(9):e0310422. pmid:39264969

Citation: The PLOS One Editors (2026) Expression of Concern: Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning. PLoS One 21(4): e0346735. https://doi.org/10.1371/journal.pone.0346735

Published: April 8, 2026

Copyright: © 2026 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.