ArticleSensors (Basel, Switzerland)2024
A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modeling and Experimental Study of Cross-Electromechanical Admittance for Crack Localization in Beams with Distributed Piezoelectric Wafers.Sensors (Basel, Switzerland) · 2026Article
- Convolutional Neural Network for Specimen-Invariant Structural Health Monitoring of FRC Under Flexural Loading.Sensors (Basel, Switzerland) · 2026Article
- Smart monitoring of composite concrete damage using EMI technique with temperature effects compensation and ANN integration.Scientific reports · 2025Article
- Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings.Sensors (Basel, Switzerland) · 2024Article
- Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.Sensors (Basel, Switzerland) · 2024Article
- Establishment of a Mass Concrete Strength-Monitoring Method Using Barium Titanate-Bismuth Ferrite/Polyvinylidene Fluoride Nanocomposite Piezoelectric Sensors with Temperature Stability.Sensors (Basel, Switzerland) · 2024Article
- Experimental Study on Dynamic Characteristics of Damaged Post-Tensioning Concrete Sleepers Using Impact Hammer.Materials (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Effective damage identification is paramount to evaluating safety conditions and preventing catastrophic failures of concrete structures. Although various methods have been introduced in the literature, developing robust and reliable structural health monitoring (SHM) procedures remains an open research challenge. This study proposes a new approach utilizing a 1-D convolution neural network to identify the formation of cracks from the raw electromechanical impedance (EMI) signature of externally bonded piezoelectric lead zirconate titanate (PZT) transducers. Externally bonded PZT transducers were used to determine the EMI signature of fiber-reinforced concrete specimens subjected to monotonous and repeatable compression loading. A leave-one-specimen-out cross-validation scenario was adopted for the proposed SHM approach for a stricter and more realistic validation procedure. The experimental study and the obtained results clearly demonstrate the capacity of the introduced approach to provide autonomous and reliable damage identification in a PZT-enabled SHM system, with a mean accuracy of 95.24% and a standard deviation of 5.64%.
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