Wave-based concrete structural health monitoring has attracted much attention. A stress wave experiences significant attenuation in concrete, however there is a lack of a unified method for predicting the attenuation coefficient of the stress wave. In this paper, a simple and effective absorption attenuation model of stress waves in concrete is developed based on the Rayleigh damping model, which indicates that the absorption attenuation coefficient of stress waves in concrete is directly proportional to the square of the stress wave frequency when the damping ratio is small. In order to verify the theoretical model, related experiments were carried out. During the experiments, a concrete beam was designed in which the d33-model piezoelectric smart aggregates were embedded to detect the propagation of stress waves. It is difficult to distinguish direct stress waves due to the complex propagation paths and the reflection and scattering of stress waves in concrete. Hence, as another innovation of this paper, a new method for computing the absorption attenuation coefficient based on the time-reversal method is developed. Due to the self-adaptive focusing properties of the time-reversal method, the time-reversed stress wave focuses and generates a peak value. The time-reversal method eliminates the adverse effects of multipaths, reflection, and scattering. The absorption attenuation coefficient is computed by analyzing the peak value changes of the time-reversal focused signal. Finally, the experimental results are found to be in good agreement with the theoretical model.
The objective of this study is to develop a new concept and methodology of smart concrete slab (SCS) with embedded tubular lead zirconate titanate transducer array for image based damage detection. Stress waves, as the detecting signals, are generated by the embedded tubular piezoceramic transducers in the SCS. Tubular piezoceramic transducers are used due to their capacity of generating radially uniform stress waves in a two-dimensional concrete slab (such as bridge decks and walls), increasing the monitoring range. A circular type delay-and-sum (DAS) imaging algorithm is developed to image the active acoustic sources based on the direct response received by each sensor. After the scattering signals from the damage are obtained by subtracting the baseline response of the concrete structures from those of the defective ones, the elliptical type DAS imaging algorithm is employed to process the scattering signals and reconstruct the image of the damage. Finally, two experiments, including active acoustic source monitoring and damage imaging for concrete structures, are carried out to illustrate and demonstrate the effectiveness of the proposed method.
Piezoceramic induced Lamb waves are often used for imaging based damage detection, especially for plate like structures. The dispersion effect of the Lamb waves deteriorates the performance of most of imaging methods, since the waveform of the dispersion signals will spread out. In this paper, an imaging method which can compensate the dispersion is developed. In the proposed method, the phase induced by the propagation distance is compensated firstly. After that, the phase deviation generated by the dispersion effect is compensated. Via the two compensations, the proposed method can derive an accurate location of the target with a clean imaging map. An experiment using a plate like structure with four piezoceramic transducer was conducted. In the experiment, the four piezoceramic sensors were used to obtain the signals of the scatterer that simulated the damage on an aluminum plate. The experimental results show that since the dispersion effect is compensated, the target’s image based on the proposed method is about 10 cm × 14 cm, which is about a quarter of that of using the back-projection imaging method.
Due to their multiple advantages, piezoceramic materials have been widely used in structural health monitoring (SHM). Piezoceramic patch-based smart aggregate (SA) and spherical piezoceramic-based smart aggregate (SSA) have been developed for damage detection of concrete structures. However, the stress waves generated by these two types of transducers are limited by their geometry and are unsuitable for use in two-dimensional concrete structures (e.g., shear walls, floors and cement concrete pavements). In this paper, a novel embeddable tubular smart aggregate (TSA) based on a piezoceramic tube was designed, fabricated and tested for use in two-dimensional (2D) structures. Due to its special geometry, radially uniform stress waves can be generated, and thus the TSA is suitable for damage detection in planar structures. The suitability of the transducer for use in structural health monitoring was investigated by characterizing the ability of the transducer to transmit and measure stress waves. Three experiments, including impedance analysis, time of arrival analysis and sweep frequency analysis, were conducted to test the proposed TSA. The experimental results show that the proposed TSA is suitable for monitoring the health condition of two-dimensional concrete structures.
In this article, a damage localization method in concrete materials based on time reversal theory and meso-scale finite element simulation considering random heterogeneous properties is developed. In this article, concrete is regarded as a multiphase composite material consisting of cement mortar matrix, coarse aggregates, and interface transition zones. Compared to other methods, which assume that concrete is homogeneous, the meso-scale model considers the intricacies of concrete inhomogeneity and can therefore better characterize the interaction between stress waves and internal structures of concrete material. Through the meso-scale method, acoustic phenomena including reflection, transmission, and diffraction among internal structures of concrete can be modeled. Furthermore, a novel time reversal based, damage imaging method is developed using the envelope of the refocused damage scattering signal to monitor the health condition of concrete. The scattered signal received by each sensor is time reversed and reemitted via numerical computation. To decrease the dispersion effect, the autocorrelation function of the refocused signals is computed to generate an image of the estimated damage. A time correction factor is introduced to decrease the influence of the elongated wave packet. Numerical and experimental results indicate that the proposed damage imaging method can locate damage with high spatial resolution in heterogeneous concrete material. Moreover, owing to the meso-scale modeling, the propagation of high-frequency stress waves in concrete can be analyzed more accurately.
Corrosion monitoring of steel bars has drawn extensive attention in recent decades. Conventional ultrasonic method, utilizing direct waves to detect damage, is adequate for severe pitting corrosion but suffers from low sensitivity to incipient pitting corrosion. Coda wave technique, a very sensitive method to subtle changes in medium using later arrival wave packets, is innovatively introduced to monitor pitting corrosion of steel bars, especially in the early stages. The decorrelation coefficient (DC) values are calculated to quantify the variations of both direct waves and coda waves. To overcome the limitations of coda waves for severe pitting corrosion and remedy the low sensitivity of direct waves for incipient pitting corrosion, a feature-level data fusion strategy is proposed to integrate the two probing waves to monitor all-stage pitting corrosion of steel bars. The combination of direct waves and coda waves could exploit the complementary merits in various pitting corrosion configurations. The proposed feature-level fusion strategy of ultrasonic coda waves and direct waves intercepted from the same recorded signals opens a new perspective in all-stage pitting corrosion monitoring of steel bars and contributes a novel scheme for whole-process damage evaluation of structures.
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