Identification of Minor Structural Damage Based on Electromechanical Impedance Sensitivity and Sparse Regularization

Fan, Xingyu; Li, Jun; Hao, Hong; Ma, Shenglan

doi:10.1061/(asce)as.1943-5525.0000892

Cited by 37 publications

(28 citation statements)

References 49 publications

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“…Tikhonov regularization is one of the most popular regularization techniques that can damp out the small measurement errors and single out a relatively accurate solution [15]. So far, it has been widely applied to damage detection [16][17][18] and other related research, such as model updating [19,20] and load identification [21]. Björck [22] gave a general form of Tikhonov regularization as follows:…”

Section: Introductionmentioning

confidence: 99%

“…In many previously developed methods, ξ 2 is selected using an L-curve criterion [12,15] or the generalized cross-validation method [20,23], but both methods are time-consuming computational tasks. Besides, the regularization parameter L is usually selected as an identity operator and thus leads to over-smoothing of the solution, which goes against the sparse feature of isolated damage at the early stage of structural deterioration [18]. Wang et al [24] proposed the iterative Tikhonov regularization (ITR) method specialized for the identification of the isolated damage to structures.…”

Section: Introductionmentioning

confidence: 99%

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Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Wang

Guo

et al. 2020

Applied Sciences

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This paper presents a robust damage identification scheme in which damage is predicted by solving the cross-modal strain energy (CMSE) linear system of equations. This study aims to address the excessive equations issue faced in the assemblage of the CMSE system. A sensitivity index that, to some extent, measures how the actual damage level vector satisfies each CMSE equation, is derived by performing an analysis of the defined residual’s sensitivity to damage. The index can be used to eliminate redundant equations and enhance the robustness of the CMSE system. Moreover, to circumvent a potentially ill-conditioned problem, a previously published iterative Tikhonov regularization method is adopted to solve the CMSE system. Some improvements to this method for determining the iterative regularization parameter and regularization operator are given. The numerical robustness of the proposed damage identification scheme against measurement noise is proved by analyzing a 2-D truss structure. The effects of location and extent of damage on the damage identification results are investigated. Furthermore, the feasibility of the proposed scheme for damage identification is experimentally validated on a beam structure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Wang

Guo

et al. 2020

Applied Sciences

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“…Piezoceramic material, with its unique sensing and actuating capacity [8,9,10], high bandwidth [11,12,13,14], low cost, and wide availability in different shapes and sizes, can be used to build transducers to generate and detect stress waves for a variety of purposes [15,16,17,18], including stress wave-based communication [19] and energy harvesting [20]. Recently, some portable, real-time and wireless systems using piezoelectric transducers have been developed successfully for structural health monitoring (SHM) applications [21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Design of a New Stress Wave-Based Pulse Position Modulation (PPM) Communication System with Piezoceramic Transducers

Ren

et al. 2019

Sensors

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Stress wave-based communication has great potential for succeeding in subsea environments where many conventional methods would otherwise face excessive difficulty, and it can benefit logging well by using the drill string as a conduit for stress wave propagation. To achieve stress wave communication, a new stress wave-based pulse position modulation (PPM) communication system is designed and implemented to transmit data through pipeline structures with the help of piezoceramic transducers. This system consists of both hardware and software components. The hardware is composed of a piezoceramic transducer that can generate powerful stress waves travelling along a pipeline, upon touching, and a PPM signal generator that drives the piezoceramic transducer. Once the transducer is in contact with a pipeline surface, the generator integrated with an amplifier is utilized to excite the piezoceramic transducer with a voltage signal that is modulated to encode the information. The resulting vibrations of the transducer generates stress waves that propagate throughout the pipeline. Meanwhile, piezoceramic sensors mounted on the pipeline convert the stress waves to electric signals and the signal can be demodulated. In order to enable the encoding and decoding of information in the stress wave, a PPM-based communication protocol was integrated into the software system. A verification experiment demonstrates the functionality of the developed system for stress wave communication using piezoceramic transducers and the result shows that the data transmission speed of this new communication system can reach 67 bits per second (bps).

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“…The minimization of l 1 norm can be solved by using the internal point method, iterative shrinkage threshold algorithm, and separable approximation method . Very recently, the sparse regularization technique has been applied to solve various problems in civil engineering, such as damage identification and load identification, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Bridge influence line identification based on adaptive B‐spline basis dictionary and sparse regularization

Chen

Yang

et al. 2019

Struct Control Health Monit

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Bridge influence line (BIL) is a promising tool for the real applications in the fields of bridge weight-in-motion (BWIM), model updating, damage identification, and load carrying capacity evaluation. The key of such applications is how to obtain the accurate results of BIL. To accurately identify BIL based on bridge dynamic responses induced by a moving vehicle, two critical problems, including how to construct a general representation function of BIL and how to deal with the ill-posed inverse problem, should be properly resolved. This paper proposes a novel approach based on the adaptive B-spline basis dictionary and sparse regularization technique for BIL identification. A representation of basis function is first established to construct BIL, and then integrated with a redundant B-spline basis dictionary to ensure the sparsity of solution. A curvature-based adaptive node optimization method is proposed to automatically adjust the spatial arrangement of nodes according to the shape of BILs. Numerical and experimental validations are conducted to verify the accuracy and robustness of the proposed approach. The identified BIL results are accurate, indicating that the proposed node-adaptive optimization and sparse regularization techniques are effective to improve the quality of BIL identification. It is also shown that the proposed approach is not sensitive to the noise interference and configuration of testing vehicle. Through the robustness testing, it is proved that the proposed approach has the merits of high accuracy and strong robustness. KEYWORDS adaptive optimization, BIL identification, B-spline basis dictionary, inverse problem, sparse regularization

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Identification of Minor Structural Damage Based on Electromechanical Impedance Sensitivity and Sparse Regularization

Cited by 37 publications

References 49 publications

Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Robust Structural Damage Detection Using Analysis of the CMSE Residual’s Sensitivity to Damage

Design of a New Stress Wave-Based Pulse Position Modulation (PPM) Communication System with Piezoceramic Transducers

Bridge influence line identification based on adaptive B‐spline basis dictionary and sparse regularization

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