In-situ strain monitoring of concrete columns using multi-layer graphene-filled cementitious sensors

Self-sensing cementitious composites provide a promising solution for real-time structural performance assessment. In this study, 12 steel-reinforced concrete columns and 6 unconfined columns embedded with multi-layer graphene (MLGs) filled cement-based sensors (GCBS) were tested to analyse the accuracy of the monitoring results. The experimental results demonstrated that GCBSs containing 5 vol% MLGs exhibited satisfactory piezoresistive performance under both cyclic and monotonic loadings during calibration. When embedded in the columns, the sensors showed a strong correlation between the fractional change in resistivity and concrete strain, with satisfactory reproducibility and stability under various loading scenarios. However, the sensor data deviated from strain gauge measurements, particularly after the concrete entered the plastic stage. This discrepancy is attributed to variations in the sensor gauge factor caused by confinement effects from the surrounding concrete. Moreover, the sensor data exhibited opposing deviation trends in unconfined columns compared to steel-reinforced columns. Consequently, a dynamic monitoring model for concrete strain was developed, explicitly accounting for the compatibility between the embedded sensor and the surrounding concrete under different strengths and stirrup reinforcement ratios. Comparative analysis reveals that the theoretical predictions show excellent agreement with experimental results throughout the entire loading process. This confirms the validity and applicability of GCBSs for the dynamic monitoring of concrete under varying loading and reinforcement conditions.

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Publication Details

Journal
Construction and Building Materials
Published
2026-09-19
DOI
https://doi.org/10.1016/j.conbuildmat.2026.148213
Primary Topic
Smart Materials for Construction
Type
article
Field-Weighted Citation Impact
0.00

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article

In-situ strain monitoring of concrete columns using multi-layer graphene-filled cementitious sensors

Minglei Wang, Tao Wu, Jian Li, Lixin Sun et al.
Construction and Building Materials
Smart Materials for Construction
article

In-situ strain monitoring of concrete columns using multi-layer graphene-filled cementitious sensors

Minglei Wang, Tao Wu, Jian Li, Lixin Sun, Xiaohu Yu, Yichen Liu
article en

Abstract

Self-sensing cementitious composites provide a promising solution for real-time structural performance assessment. In this study, 12 steel-reinforced concrete columns and 6 unconfined columns embedded with multi-layer graphene (MLGs) filled cement-based sensors (GCBS) were tested to analyse the accuracy of the monitoring results. The experimental results demonstrated that GCBSs containing 5 vol% MLGs exhibited satisfactory piezoresistive performance under both cyclic and monotonic loadings during calibration. When embedded in the columns, the sensors showed a strong correlation between the fractional change in resistivity and concrete strain, with satisfactory reproducibility and stability under various loading scenarios. However, the sensor data deviated from strain gauge measurements, particularly after the concrete entered the plastic stage. This discrepancy is attributed to variations in the sensor gauge factor caused by confinement effects from the surrounding concrete. Moreover, the sensor data exhibited opposing deviation trends in unconfined columns compared to steel-reinforced columns. Consequently, a dynamic monitoring model for concrete strain was developed, explicitly accounting for the compatibility between the embedded sensor and the surrounding concrete under different strengths and stirrup reinforcement ratios. Comparative analysis reveals that the theoretical predictions show excellent agreement with experimental results throughout the entire loading process. This confirms the validity and applicability of GCBSs for the dynamic monitoring of concrete under varying loading and reinforcement conditions.

Construction and Building MaterialsVol. 543
Chang'an University (CN), Langfang Normal University (CN)
National Natural Science Foundation of China, Department of Education of Hebei Province, Langfang Municipal Science and Technology Bureau, National University's Basic Research Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 22%
Smart Materials for Construction
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