Piezoresistive Carbon Nanotube-Concrete Composites Coupled with Embedded IoT Networks: A Dual-Layer Architecture for Continuous Seismic Structural Health Monitoring in Smart Cities
Abstract
Abstract Background The integration of multi-walled carbon nanotubes (MWCNTs) into cementitious composites enables self-sensing concrete with intrinsic piezoresistive properties. In seismic nations such as Peru (Pacific Ring of Fire), real-time monitoring of critical infrastructure is essential for public safety and smart city development. Methods A mixed micromechanics-finite element modeling approach characterized MWCNT-concrete piezoresistive behavior. A three-dimensional piezoresistivity matrix incorporating quantum tunneling effects and effective conductive path models was established. A four-layer hierarchical IoT architecture was designed for continuous data acquisition and anomaly detection. Results MWCNTs at 0.1–0.5 wt% reduced electrical resistivity by 63–78%, with percolation threshold 0.11 vol%. Self-sensing concrete achieved gauge factors (GF) 150–748 and stress sensitivity 2.87%/MPa. Embedded sensors in RC beams showed R2 = 0.96 correlation with conventional strain gauges. The IoT framework achieved 95.3% accuracy in seismic load simulations. Conclusions CNT-based self-sensing concrete with IoT networks constitutes a scalable, cost-effective solution for continuous SHM in seismic regions, fully compatible with Peru NTE E.030 seismic code.
Read the paper
Related studies
- Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
- Physics of Partially Saturated Porous Media: Residual Saturation and Seismic-Wave Propagation
- Modern Imaging Using Seismic Reflection Data
- Effects of marine seismic surveys on free-ranging fauna: a systematic literature review
- A review of the recent seismic activity in the southern part of egypt (upper egypt)
- A review of research on mechanism of seismic attenuation and energy compensation
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.