Sub-Structural Parameter Identification Including Cracks of Beam Structure Using PZT Patch

Jinesh, N.; Shankar, K.

doi:10.1080/15502287.2019.1569174

Cited by 5 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A plane frame with nine structural members is considered in this example which was solved by Jinesh and Shankar. 17 The global structure of the frame is supported at two nodes as shown in Figure 8(a). The structure is modeled with nine frame elements with six nodes, and each has three DOFs.…”

Section: Example 2: Ss Of a Frame With Crackmentioning

confidence: 99%

“…In STMC method, the same crack depth is identified with an absolute error of 1.01% and 4.11% with complete and incomplete sets of responses without noise, respectively. The same structure was used in crack damage detection using PZT patches by Jinesh and Shankar, 17 identified the crack parameters of smallest depth of j = 0:05 with an absolute error of 11.6% without noise and 17.1% with 5% noise. The STMC identified the same crack depth with an error (incomplete set of responses) of 4.11% without noise and 8.35% with 5% noise.…”

Section: Example 2: Ss Of a Frame With Crackmentioning

confidence: 99%

See 1 more Smart Citation

Crack damage detection of structures using spectral transfer matrix

Nandakumar

Shankar

2020

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

A novel spectral transfer matrix for a cracked beam element is developed in this article and the same is used to identify the crack parameters on the beam structures. Spectral transfer matrix is developed from trigonometric functions based on the theory of fracture mechanics. This matrix determines the natural frequencies of a structure with crack with better accuracy than any other transfer matrices in the literature. The state vector at a node on the structure is formed which includes the displacement, rotation, internal and external forces, and moments at that node. When the state vector is multiplied with the transfer matrix, the state vector at the adjacent node is obtained. Each element is assumed to have a single open breathing crack with unknown depth and location. Initially, the developed spectral transfer matrix is used to determine the natural frequencies of a known cantilever, and after successful validation, the same is used for crack damage detection. By an inverse approach, crack parameters in each element are identified. The state vector at one node on the structure is obtained by measurement of input and out responses which is known as the initial state vector. Acceleration responses at selected nodes on the structure are measured and the state vectors at those nodes are predicted using spectral transfer matrices. The mean square error between measured and simulated responses is minimized using a heuristic optimization algorithm, with crack depth and location in each element as the optimization variables. Spectral transfer matrix method is applied to two numerical problems with single crack in each element; later, this method is successfully validated experimentally with structures having different boundary conditions. The accuracy in identified crack parameters and the applicability to sub-structures of a large structure are the important aspects of this method.

show abstract

Section: Example 2: Ss Of a Frame With Crackmentioning

confidence: 99%

Section: Example 2: Ss Of a Frame With Crackmentioning

confidence: 99%

Crack damage detection of structures using spectral transfer matrix

Nandakumar

Shankar

2020

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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%.

show abstract

“…Traditional structural crack detection mostly relies on human vision inspection, which is time and labor consuming. Some nondestructive test (NDT) methods, such as piezoelectricity transducer (PZT) sensors [1,2] and eddy current-based technologies [3] etc., are also effective to detect structural cracks given that the proximate positions of potential cracks are known as a priori. Recently, with fast development of (unmanned aerial vehicle) UAV and computer vision recognition technologies [4], the surface images of large civil structures can be more easily collected and be more efficiently processed to quickly detect structural cracks.…”

Section: Introductionmentioning

confidence: 99%

Structural Crack Detection Using DPP-BOTDA and Crack-Induced Features of the Brillouin Gain Spectrum

Zhang

Yang

Xu³

et al. 2020

Sensors

View full text Add to dashboard Cite

Structural damage generally initiates in the form of structural cracks. Thus, developing efficient crack detection techniques is of great importance for the structural health monitoring. In this paper, a new crack identification method is proposed, which is based on the differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) technology and the irregular features of Brillouin gain spectrum (BGS) in the fiber due to structural cracks. The proposed method provides a new way to detect and quantify structural cracks without knowing the strain in the structure. First, the working mechanism of DPP-BOTDA is introduced to illustrate the reason that the DPP-BOTDA, compared to traditional BOTDA technique, can significantly improve the spatial resolution of distributed strain sensing, which is critical for structural crack detection. Then, the BGSs in the fiber with the presence of structural cracks, measured by the DPP-BOTDA, are numerically simulated, from which the crack-induced irregular features of the BGS are summarized. Based these irregular features, new structural crack detection and quantification methods are proposed, which are found to be independent of structural stain. Finally, an experiment is conducted on a simple supported reinforced concrete (RC) beam. The results demonstrate that by using the BGS measured by the DPP-BOTDA, the proposed structural crack identification method successfully detects the occurrence of structural cracks and relatively accurately predicts the crack widths.

show abstract

Sub-Structural Parameter Identification Including Cracks of Beam Structure Using PZT Patch

Cited by 5 publications

References 29 publications

Crack damage detection of structures using spectral transfer matrix

Crack damage detection of structures using spectral transfer matrix

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

Structural Crack Detection Using DPP-BOTDA and Crack-Induced Features of the Brillouin Gain Spectrum

Contact Info

Product

Resources

About