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Prediction and prevention of concrete chloride penetration: machine learning and MICP techniques.

Authors :
Lianqiang Li
Le Su
Bingchuan Guo
Rongjiang Cai
Xi Wang
Tao Zhang
Source :
Frontiers in Materials; 2024, p1-15, 15p
Publication Year :
2024

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22968016
Database :
Complementary Index
Journal :
Frontiers in Materials
Publication Type :
Academic Journal
Accession number :
179425377
Full Text :
https://doi.org/10.3389/fmats.2024.1445547