Capacitance-based self-sensing response of smart ultra-high-performance concrete under cyclic and monotonic compressive loading
Source abstract
Abstract
Smart ultra-high-performance concrete (S-UHPC) has received increasing attention as a self-sensing material for structural health monitoring; however, conventional electrical resistance-based self-sensing often exhibits signal instability under repeated loading. To address this limitation, this study investigates the capacitance-based self-sensing response of S-UHPC incorporating steel fibers and fine steel slag aggregates, providing a systematic comparison with electrical resistance-based self-sensing response under various loading conditions (no-load, cyclic compression, and monotonic compression). Both capacitance and electrical resistance were measured simultaneously under alternating current across a range of frequencies (10–900 kHz) and voltages (1–5 V). Under no-load conditions, both capacitance and electrical resistance decreased with increasing frequency, while the effects of voltage were significantly less pronounced than the effects of frequency. Under cyclic compressive loading (up to 40 MPa), the fractional change in capacitance ( FCC ) exhibited a stable, reversible response with minimal signal variability, whereas the fractional change in electrical resistance ( FCR ) showed larger cycle-to-cycle fluctuations. Under monotonic compressive loading, FCC increased until failure, whereas FCR decreased. These contrasting trends reflect distinct governing mechanisms: dielectric polarization for capacitance and the evolution of conductive pathways for electrical resistance. Capacitance-based self-sensing demonstrates superior stability and repeatability in S-UHPC, highlighting its potential for reliable stress monitoring in high-performance cementitious composites.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Materials for Construction · Concrete Corrosion and Durability · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
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