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Journal article
Artificial neural network modeling of self-compacting concrete mixed and cured with seawater for compressive strength and chloride penetration prediction
Journal of structural integrity and maintenance, Vol.10(4), 2558425
Autumn 2025
Web of Science ID: WOS:001574727700001
Abstract
The large-scale use of concrete requires reliable quality assessment to ensure workability, mechanical properties, and durability. Conventional testing methods are often costly and time-consuming. This study explores predictive modeling as an efficient alternative, focusing on self-compacting concrete (SCC) produced and cured with seawater, silica fume, and fly ash. Workability indicators, including slump flow, J-ring, visual stability index (VSI), and air content, were used to predict compressive strength and chloride concentration. Artificial neural networks (ANNs) and Classification and Regression Trees (CART) were applied. The ANN models achieved high accuracy, with compressive strength predicted at a minimum mean squared error (MSE) of 0.085638. The chloride content prediction achieved an R² of 0.9429. CART analysis revealed that air content was the most significant factor influencing compressive strength, while the J-ring had the strongest impact on chloride content. A comparative study demonstrated that ANNs outperformed random forest regression in predictive capability. These results highlight the value of machine learning in concrete research, offering a cost-effective and time-saving method for property evaluation. The findings also support the sustainable use of seawater and supplementary cementitious materials in the production of concrete. The novelty of this study lies in predicting the compressive strength and chloride ion concentration of self-compacting concrete produced and cured with seawater and pozzolans. Neural networks and machine learning were applied for this prediction, an approach not previously explored.
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Details
- Title
- Artificial neural network modeling of self-compacting concrete mixed and cured with seawater for compressive strength and chloride penetration prediction
- Publication Details
- Journal of structural integrity and maintenance, Vol.10(4), 2558425
- Resource Type
- Journal article
- Publisher
- Taylor & Francis
- Number of pages
- 17
- Copyright
- © 2025, Taylor & Francis
- Identifiers
- WOS:001574727700001; 99381500994406600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English