Piezo-impedance based fatigue damage monitoring of restrengthened concrete frames

Haq, Moinul; Bhalla, Suresh; Naqvi, Tabassum

doi:10.1016/j.compstruct.2021.114868

Cited by 19 publications

(3 citation statements)

References 50 publications

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“…A large number of existing research studies in the broader literature have examined the effectiveness of EMI-based SHM of concrete due to several degradation causes, including the corrosion of steel bars in RC elements [ 14 , 15 , 16 , 17 ], concrete cover loss [ 18 ], the influence of the heating time [ 19 ], artificial cracks, and mass loss [ 20 , 21 ]. Furthermore, real-scale members of RC structures have been examined, such as beams [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], frames [ 31 ], and joints [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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

Sapidis,

Kansizoglou,

Naoum

et al. 2024

Sensors

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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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Section: Introductionmentioning

confidence: 99%

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

Sapidis,

Kansizoglou,

Naoum

et al. 2024

Sensors

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“…(2) By jointly controlling curing temperature and pressure, high integration reliability and performance consistency can be achieved. (3) The method is also universally applicable for complex composite structure forms or other different kinds of materials.…”

Section: Introductionmentioning

confidence: 99%

In-situ integration and performance verification of large-scale PZT network for composite aerospace structure

Ren

zhang

Yuan

et al. 2023

Smart Mater. Struct.

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Piezoelectric transducer (PZT) based structural health monitoring (SHM) technology has been proved to be effective in increasing safety and reliability of composite aircraft structures. However, the attachment of PZT network to the host structure is considered as a weak link when facing the long term durability requirement of aerospace SHM, which should be overcome for aerospace SHM. In this paper, a surface-mounted co-curing method is creatively proposed to realize in-situ integration of large-scale PZT network and composite structure. By jointly controlling curing temperature and pressure, the proposed method can realize integration of large-scale PZT network and structural surface with high reliability and performance consistency. Compared with the conventional integration methods, the proposed method does not affect the manufacturing process or reduce the mechanical property of structure, and can be easily implemented whenever the host structure is available. To evaluate the effectiveness of the proposed integration method, a large-scale PZT network with an overall dimension of 1100mm×600mm is integrated with a carbon fiber composite panel of wing box. The integration caused influence on functional integrity is first assessed by performing electro-mechanical impedance (EMI) based theoretical analysis and experimental investigation. Then guided wave (GW) signals of the integrated large-scale PZT network are also acquired and analyzed, which proves the good signal repeatability and consistency of the network. At last, the SHM performance of the network is verified by conducting impact damages on the composite panel and performing damage monitoring, experimental results show that accurate monitoring is realized.

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“…However, despite many advances in SHM techniques, most methods involve a limited number of sensors, such as strain gauges, piezoelectric ceramics, and optic sensors, distributed over a wide range of infrastructures [4,10,11]. These monitoring techniques create local low-resolution detection systems for concrete structures [12][13][14]. Furthermore, these sensors have serious drawbacks, such as low durability, poor sensitivity, high cost of production, high time consumption, installation costs, and difficulties in assuring proper bond conditions with cementitious composite [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional braided composites as innovative smart structural reinforcements

Abedi

Hassanshahi²,

Barros³

et al. 2022

Composite Structures

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Piezo-impedance based fatigue damage monitoring of restrengthened concrete frames

Cited by 19 publications

References 50 publications

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

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

In-situ integration and performance verification of large-scale PZT network for composite aerospace structure

Three-dimensional braided composites as innovative smart structural reinforcements

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