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Made available in DSpace on 2022-04-29T08:46:05Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Structural health monitoring techniques are widely used in industry applications to guarantee the integrity of several types of components. This article proposes a baseline-free damage imaging algorithm based on Spatial Frequency Domain Virtual Time Reversal (SFD-VTR). Virtual time reversal (VTR) is an alternative to traditional time reversal as it reduces the burden of physically back-propagating re-emitted signals by applying signal operations between the transmitted and received waveforms. However, VTR rely on the reconstruction of the emitted signal in the time domain, which involves significant data manipulation causing sampling errors of reconstructed signals. Novel SFD-VTR damage indices were here proposed to enhance defect detection as they do not require the time domain reconstruction of re-emitted signals. Experimental results showed that the proposed algorithm was able to localise material flaws and can be use as Human Machine Interface (HMI). Universidade Estadual Paulista, 28108 Sao Paulo, Brazil, 01049-010 (e-mail: bruno.castro@unesp.br) Electrical Engineering, Universidade Estadual Paulista, 28108 Bauru, SP, Brazil, 17033360 (e-mail: f.baptista@unesp.br) Electrical Engineering, UTFSM, 28090 Santiago, Chile, 8940000 (e-mail: jorge.ardila@usm.cl) University of Surrey, 3660 Guildford, Surrey, United Kingdom of Great Britain and Northern Ireland, GU2 7XH (e-mail: f.ciampa@surrey.ac.uk)

Traditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wave strategy utilizing adaptive multiple signal classification (MUSIC) and sparse reconstruction of dispersion reconstruction is proposed. The multimodal Lamb waves are initially reconstructed in the f-k domain using random measurements, allowing for the identification and characterization of multimodal Lamb waves. Then, using local polynomial expansion and derivation, the phase and group velocities for each Lamb wave mode could be computed. Thus, the steering vectors of all potential scattering Lamb waves for each grid in the scanning area can be established, thereby allowing for the formulation of the MUSIC algorithm. To increase the precision and adaptability of the MUSIC method, the local wave components resulting from potential scatters are extracted with an adaptive window, which is governed by the group velocities and distances of Lamb wave propagation. As a result, the reconstructed dispersion relations and windowed wave components can be used to highlight the scattering features. For the method investigation, both a simulation and experiment are carried out, and both the dispersion curves and damage locations can be detected. The results demonstrate that damage localization is possible without theoretical dispersion data and baseline recordings while exhibiting a considerable accuracy and resolution.

The DataSet contains the results of the mechanical behaviour of a concrete cube with dimensions 70 x 70 x 70 cm3under the splitting test. Three cubes were tested: cube 1 (concrete with polymer fibres), cube 2 (concrete with steel fibres) and cube 3 (plain concrete).

The DataSet contains guided wave signals measured in adhesive joints of steel plates (steel of type S355 with a thickness of 11.9 mm ) and carbon fibre reinforced polymer (CarboDur S1012 with a thickness of 1.24 mm). The samples were as follows: #0 – intact; #1 – 10% disbond; #2 – 20% disbond; #3 – 50% disbond; #4 – 80% disbond; #5 – 100% disbond.

Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localization with respect to resolution. To detect damage with fewest sensors and high resolution, a method, using only two piezoelectric transducers and based on orthogonal matching pursuit (OMP) decomposition, was proposed. First, an OMP-based decomposition and dispersion removal algorithm is introduced, which is capable of separating wave packets of different propagation paths and removing the dispersion part successively. Then, two simulation signals, with nonoverlapped and overlapped wave packets, are employed to verify the proposed method. Thereafter, with the proposed algorithm, the wave packets reflected from the defect and edge are all separated. Finally, a sparse sensor array with only two transducers succeeds in localizing the defect. The experimental results show that the OMP-based algorithm is beneficial for resolution improvement and transducer usage reduction.

Reconstructing damage geometry with computationally efficient algorithms is of primary importance in establishing a robust structural health monitoring system (SHMS). In this paper electromagnetic migration, a linearized imaging algorithm, is adopted to image the damages in reinforced concrete structures. This algorithm is formulated in time-domain for 3-D inhomogeneous isotropic and lossy structures. In order to reduce the computational cost and to examine the damage resolution of this imaging algorithm, different imaging conditions are introduced. Numerical simulations in 2-D transverse magnetic (TM) wave for a reinforced concrete slab with multiple damages are performed to test the effectiveness of the algorithm. All synthetic sensor data, incident field, and migration field are computed via a finite difference time-domain (FDTD) method. It is concluded that the proposed imaging algorithm is capable of efficiently identifying the damages geometries, is robust against measurement noise, and may be employed in a SHMS.