A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers

被引：27

作者：

Sapidis, George M. ^{[1
]}

Kansizoglou, Ioannis ^{[2
]}

Naoum, Maria C. ^{[1
]}

Papadopoulos, Nikos A. ^{[1
]}

Chalioris, Constantin E. ^{[1
]}

机构：

[1] Democritus Univ Thrace, Lab Reinforced Concrete & Seism Design Struct, Struct Engn Sci Div, Civil Engn Dept,Sch Engn, Xanthi 67100, Greece

[2] Democritus Univ Thrace, Dept Prod & Management Engn, Sch Engn, V Sofias 12, Xanthi 67132, Greece

来源：

SENSORS | 2024年 / 24卷 / 02期

关键词：

structural health monitoring (SHM); concrete damage identification; convolutional neural network (CNN); 1-D CNN; damage classification; deep learning; electromechanical impedance (EMI); fiber-reinforced concrete (FRC); piezoelectric lead zirconate titanate (PZT); STRESS;

D O I：

10.3390/s24020386

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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%.

引用

页数：15

共 68 条

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