Abstract
Ultra-high performance seawater sea sand concrete (UHPSSC) contains a high volume of unhydrated particles, conferring superior autogenous self-healing potential. This study quantitatively evaluates the healing kinetics of UHPSSC under the coupled influences of crack width, environmental humidity, temperature, and diverse aqueous environments. Micro-analytical evidence obtained via XRD and SEM elucidates the underlying healing mechanisms of UHPSSC in freshwater and seawater environments. Results indicate that 100 μm cracks closed completely within 28 days under freshwater immersion. While elevated temperatures accelerate initial healing kinetics, a 60 °C environment eventually limits late-stage hydration. Notably, UHPSSC demonstrates enhanced healing efficiency compared to traditional UHPC across all environmental conditions. The self-healing products primarily consist of CaCO3, ettringite, Mg(OH)2, and C-S-H gel, with the morphologies influenced by environmental ion concentrations. Additionally, five machine learning models were established and compared, with the artificial neural network (ANN) demonstrating optimal predictive accuracy (R2 = 0.956). Furthermore, an interpretable ANN-SHAP framework was implemented to decouple the complex multi-factor interactions, identifying initial crack width as the dominant geometric constraint. This research provides an interpretable data-driven framework for assessing the self-healing performance of UHPSSC in complex marine environments.
| Original language | English |
|---|---|
| Article number | 116266 |
| Journal | Journal of Building Engineering |
| Volume | 126 |
| DOIs | |
| State | Published - 15 May 2026 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 14 Life Below Water
Keywords
- Machine learning
- Rehydration
- SHAP analysis
- Self-healing
- Ultra-high performance seawater sea sand concrete
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