Self-sensing of load and deformation in concrete using intrinsic capacitance and resistance

This study investigates the electromechanical behavior and self-sensing performance of plain and reinforced concrete specimens under flexural loading through real-time electrical capacitance and resistance measurements. The primary objective is to assess the feasibility of using these electrical parameters as indicators of stress and deformation without relying on conductive fillers and external or embedded sensors. Concrete and reinforced concrete beams were prepared with standard mix proportions, and electrical measurements were conducted during three-point bending tests using an LCR meter operating at 2 kHz. Experimental results reveal a strong linear correlation between mechanical load and fractional changes in both electrical capacitance and resistance for unreinforced specimens, with R² values of 0.99 and 0.98, respectively. The peak fractional changes in these specimens reached 3.13% and 3.01%, confirming their sensitivity to stress-induced microstructural changes. In reinforced concrete specimens, which exhibited ductile behavior due to steel reinforcement, maximum changes in capacitance and resistance were observed as 8.89% ± 0.13 and 10.62% ± 1.48, respectively. Notably, capacitance demonstrated higher correlation with structural deflection (R² = 0.97 ± 0.02), whereas resistance was more responsive to crack propagation but exhibited greater signal fluctuation. These findings establish capacitance as a stable indicator for elastic deformation and resistance as a damage-sensitive signal. The proposed method enables continuous, non-destructive damage detection and deformation tracking in both plain and reinforced concrete, making it suitable for integration into future innovative infrastructure systems.

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Publication Details

Journal
Scientific Reports
Published
2025-11-14
DOI
https://doi.org/10.1038/s41598-025-23576-w
Primary Topic
Smart Materials for Construction
Type
article
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article

Self-sensing of load and deformation in concrete using intrinsic capacitance and resistance

Murat Öztürk, Asiye Akis
Scientific Reports
Smart Materials for Construction
article

Self-sensing of load and deformation in concrete using intrinsic capacitance and resistance

Murat Öztürk, Asiye Akis
article en

Abstract

This study investigates the electromechanical behavior and self-sensing performance of plain and reinforced concrete specimens under flexural loading through real-time electrical capacitance and resistance measurements. The primary objective is to assess the feasibility of using these electrical parameters as indicators of stress and deformation without relying on conductive fillers and external or embedded sensors. Concrete and reinforced concrete beams were prepared with standard mix proportions, and electrical measurements were conducted during three-point bending tests using an LCR meter operating at 2 kHz. Experimental results reveal a strong linear correlation between mechanical load and fractional changes in both electrical capacitance and resistance for unreinforced specimens, with R² values of 0.99 and 0.98, respectively. The peak fractional changes in these specimens reached 3.13% and 3.01%, confirming their sensitivity to stress-induced microstructural changes. In reinforced concrete specimens, which exhibited ductile behavior due to steel reinforcement, maximum changes in capacitance and resistance were observed as 8.89% ± 0.13 and 10.62% ± 1.48, respectively. Notably, capacitance demonstrated higher correlation with structural deflection (R² = 0.97 ± 0.02), whereas resistance was more responsive to crack propagation but exhibited greater signal fluctuation. These findings establish capacitance as a stable indicator for elastic deformation and resistance as a damage-sensitive signal. The proposed method enables continuous, non-destructive damage detection and deformation tracking in both plain and reinforced concrete, making it suitable for integration into future innovative infrastructure systems.

Scientific ReportsVol. 15(1)
Bursa Uludağ Üni̇versi̇tesi̇ (TR)
Openalex Percentile: Top 73%
Smart Materials for Construction
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