Abstract
In this work, two nanomaterials (nano carbon fiber (NCF) and nano carbon black (NCB)) were respectively combined with macro-scale conductive materials (carbon fiber and steel fiber) for the strategic construction of hierarchical conductive networks in conductive ultra-high performance concrete (C-UHPC). The fluidity, electrical properties, electrochemical analysis, mechanical properties, microhardness analysis, micromorphology characterization, economic and environmental benefits were systematically evaluated to elucidate the effect of nanomaterials on C-UHPC. Results revealed that the hierarchical conductive networks dominated by different nanomaterials exhibited distinct effects on the properties of C-UHPC, along with significant differences in economic and environmental benefits. Specifically, NCF demonstrated a more superior effect on promoting the strength improvement for C-UHPC through fiber bridging with macro-scale conductive materials, but it reduced the economic and environmental benefits. In contrast, the NCB utilized its unique particulate morphology as conductive points to form a “conductive coating” within the macroscopic conductive framework, effectively improving the electrical properties of C-UHPC with high economic and environmental benefits, while significantly impairing the fluidity and 28-day compressive strength for the samples. Furthermore, machine learning algorithms were employed to predict the electrical properties of C-UHPC, with the support vector regression model demonstrating superior prediction accuracy with R2 of 0.9861. A swarm intelligence-based optimization system was also developed to support the multi-objective design of C-UHPC, considering electrical, mechanical and economic objectives. This research provides a viable pathway for developing C-UHPC with integrated multifunctionality, contributing to the intelligent regulate and sustainable management of next-generation infrastructure.
| Original language | English |
|---|---|
| Article number | 115924 |
| Journal | Journal of Building Engineering |
| Volume | 123 |
| DOIs | |
| State | Published - 1 Apr 2026 |
| Externally published | Yes |
Keywords
- AI-Driven composite design
- Electrical properties
- Hierarchical conductive network
- Mechanical properties
- Nanomaterials
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