A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers
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 rese...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/386 |
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| author | George M. Sapidis Ioannis Kansizoglou Maria C. Naoum Nikos A. Papadopoulos Constantin E. Chalioris |
| author_facet | George M. Sapidis Ioannis Kansizoglou Maria C. Naoum Nikos A. Papadopoulos Constantin E. Chalioris |
| author_sort | George M. Sapidis |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T09:47:38Z |
| format | Article |
| id | doaj.art-34c2b4f8472b40aa892a01dd402ec87a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:47:38Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-34c2b4f8472b40aa892a01dd402ec87a2024-01-29T14:14:05ZengMDPI AGSensors1424-82202024-01-0124238610.3390/s24020386A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate TransducersGeorge M. Sapidis0Ioannis Kansizoglou1Maria C. Naoum2Nikos A. Papadopoulos3Constantin E. Chalioris4Laboratory of Reinforced Concrete and Seismic Design of Structures, Structural Engineering Science Division, Civil Engineering Department, School of Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceDepartment of Production and Management Engineering, School of Engineering, Democritus University of Thrace, V. Sofias 12, 67132 Xanthi, GreeceLaboratory of Reinforced Concrete and Seismic Design of Structures, Structural Engineering Science Division, Civil Engineering Department, School of Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceLaboratory of Reinforced Concrete and Seismic Design of Structures, Structural Engineering Science Division, Civil Engineering Department, School of Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceLaboratory of Reinforced Concrete and Seismic Design of Structures, Structural Engineering Science Division, Civil Engineering Department, School of Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceEffective 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%.https://www.mdpi.com/1424-8220/24/2/386structural health monitoring (SHM)concrete damage identificationconvolutional neural network (CNN)1-D CNNdamage classificationdeep learning |
| spellingShingle | George M. Sapidis Ioannis Kansizoglou Maria C. Naoum Nikos A. Papadopoulos Constantin E. Chalioris A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers Sensors structural health monitoring (SHM) concrete damage identification convolutional neural network (CNN) 1-D CNN damage classification deep learning |
| title | A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers |
| title_full | A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers |
| title_fullStr | A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers |
| title_full_unstemmed | A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers |
| title_short | A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers |
| title_sort | deep learning approach for autonomous compression damage identification in fiber reinforced concrete using piezoelectric lead zirconate titanate transducers |
| topic | structural health monitoring (SHM) concrete damage identification convolutional neural network (CNN) 1-D CNN damage classification deep learning |
| url | https://www.mdpi.com/1424-8220/24/2/386 |
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