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ORIGINAL RESEARCH article

Front. Mater.
Sec. Structural Materials
Volume 11 - 2024 | doi: 10.3389/fmats.2024.1445547
This article is part of the Research Topic Microbial Induced Calcite Precipitation for Developing Sustainable Construction Materials View all articles

Prediction and prevention of concrete chloride penetration: Machine learning and MICP techniques

Provisionally accepted
Lianqiang Li Lianqiang Li 1Le Su Le Su 2Bingchuan Guo Bingchuan Guo 3Rongjiang Cai Rongjiang Cai 4*Xi Wang Xi Wang 4Tao Zhang Tao Zhang 4
  • 1 Tianjin Municipal Engineering Design and Research Institute Co. , Ltd, Tianjin, China
  • 2 Tianjin Binhai New Area Urban Investment Construction Development Co., Tianjin, China
  • 3 Offshore Engineering Technology Center of China Classification Society, Tianjin, 300457, P R China, Tianjin, China
  • 4 Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China

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

    The chloride migration coefficient (CMC) of concrete is crucial for evaluating its durability. This study develops ensemble models to predict the CMC of concrete, addressing the limitations of traditional, labor-intensive laboratory tests. We developed three ensemble models: an inverse variance-based model, an Artificial Neural Network (ANN)-based model, and a tree-based model using the random forest regression algorithm. These models were trained on a dataset comprising 843 concrete mix proportions from existing literature. Results indicate that ensemble models outperform single models such as ANN and Support Vector Regression (SVR) in predicting CMC, with the combined random forest and ANN model showing the highest accuracy. Sensitivity analysis using Shapley Additive Explanations (SHAP) reveals that the CMC is most influenced by the water-to-cement ratio and curing age.Additionally, we designed a graphical user interface (GUI) to facilitate the practical application of our models. This research offers a robust methodology for evaluating concrete durability and potential for extending the prediction to other concrete properties..

    Keywords: concrete, Chloride migration coefficient, machine learning, durability, GUI

    Received: 07 Jun 2024; Accepted: 08 Jul 2024.

    Copyright: © 2024 Li, Su, Guo, Cai, Wang and Zhang. 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: Rongjiang Cai, Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China

    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.