Ma, M;
Chen, M;
Zhang, T;
Zhang, M;
Cui, X;
(2025)
Research on carbonation percentage of carbonated recycled concrete fine aggregate: experimental investigation and machine learning prediction.
Journal of Sustainable Cement-Based Materials
10.1080/21650373.2024.2449491.
(In press).
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Zhang_Research on carbonation percentage.pdf - Accepted Version Access restricted to UCL open access staff until 20 January 2026. Download (1MB) |
Abstract
The carbonation percentage (CP) is an essential carbonation degree evaluation index that determines the physicochemical properties of carbonated recycled concrete fine aggregate (CRFA), and its accurate prediction is necessary. Therefore, this paper first uses six machine learning algorithms to build predictive models, aiming to forecast the CP of CRFA under various carbonation control parameters and analyze their advantages and shortcomings. Six models including k-nearest neighbor regression, back propagation neural network, decision tree (DT), random forest, gradient boosting decision trees, and extreme gradient boosting (XGB) are trained, tested, and validated by employing a comprehensive dataset collected from our 100 mix design samples. As expected, the XGB model shows a fascinating prediction ability superior to other proposed models, owing to the largest coefficient of determination being 0.974 and the lowest root mean squared of 1.521 in the testing set. Moreover, the sensitive analysis of input feature parameters is conducted using Shapley additive explanations (SHAPs) according to the XGB-based prediction model, illustrating C-Con (average SHAP value = 0.72) shows the largest impact on the development of CP. This study promotes effective forecasting and modifies the suitable design of carbonation control parameters, providing a theoretical foundation for utilizing building waste following low-carbon and ecologically friendly development concepts.
| Type: | Article |
|---|---|
| Title: | Research on carbonation percentage of carbonated recycled concrete fine aggregate: experimental investigation and machine learning prediction |
| DOI: | 10.1080/21650373.2024.2449491 |
| Publisher version: | https://doi.org/10.1080/21650373.2024.2449491 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Carbonation percentage, carbonated recycled concrete fine aggregate, machine learning, feather importance, parameter sensitivity analysis, Shapley additive explanations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204990 |
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