With a structure’s foundation and supporting ground generally being critical to its design and construction, understanding how soil behaves under various stress and drainage conditions is imperative. It is well known that certain characteristics and behaviors of soils with fines are highly dependent on water content and liquid limit is one of the important soil index properties to define such characteristics. However, conventional liquid limit measurement techniques can be easily affected by the proficiency of the operator, potentially leading to disastrous consequences. The dynamic properties of soils are required in numerous applications, and current testing techniques frequently call for specialized lab equipment, which is often pricy and delicate to test conditions. To address these concerns and advance the state of the art, this study explores a novel method to determine the liquid limit of cohesive soil by employing video-based vibration analysis which may precisely measure and identify the status of a soil’s water content. In this research, the modal characteristics of cohesive soil columns are extracted from videos by phase-based motion estimation. By utilizing the proposed method that analyzes the optical flow in every pixel of the series of frames that effectively represents the motion of corresponding points of the soil specimen, the vibration characteristics of the entire soil specimen could be assessed in a non-contact and non-destructive manner. The experimental investigation results compared with the liquid limit determined by the conventional method verify that the proposed method reliably and straightforwardly identifies the liquid limit of clay. It is envisioned that the proposed approach could be applied to measuring liquid limit of soil in practical field, entertaining its simple implementation that only requires a digital camera or even a smartphone without the need for special equipment or techniques that may be subject to the proficiency of the operator.
Among various signal processing approaches, stochastic resonance (SR) has been widely employed for weak signal detection and mechanical fault diagnosis. Various advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR. Yet, these methods often require detailed information about the original signal a priori, which is challenging from measurements that are already overwhelmed by noise. Furthermore, classical bistable SR has often been employed for weak signal detection, which exhibits an inherent signal distortion due to output saturation that reduces the signal recovery performance. To address these concerns and advance the state of the art, we propose a novel signal denoising method that exploits unsaturated SR in a parallel array of piecewise continuous bistable systems. The original noise-contaminated signal is adaptively scaled by an optimal gain value that is determined from a non-dimensional model based on the attendant noise level, which is one of the few parameters that can be reliably identified from practical noise-contaminated signals. As a result, the proposed approach can operate without any post-processing optimization and parameter selection. Numerical investigations are performed with a simulated acoustic emission signal (amplitude modulated sine pulse) with various amplitudes and attendant noise levels to illustrate the operation principle and the effectiveness of the proposed approach. The results exemplify the promising potential of implementing the proposed approach for enhancing online signal denoising in practice.
Investigating the mechanical properties of biological and biocompatible hydrogel materials has recently gained extensive research interest due to their potential applications in various fields including tissue engineering, biorobotics and sensors. However, estimating the essential structural characteristics such as elastic moduli of hydrogel structures may not be easily identified using conventional contact-based techniques such as accelerometers and strain gauges due to their additional mass loading to the structure and influence on the shape of the hydrogel structure by mechanical contact. Non-contact optical methods such as Laser-Doppler vibrometry may be able to identify the vibration characteristics; yet, the low reflectivity of translucent hydrogel’s surfaces is one of the major challenges in laser-based vibration analysis, and experimentally estimating the mode shape requires significant effort. In this study, we aim to investigate a contactless method to simultaneously identify the Young’s and shear moduli of hydrogel structures by employing video-based vibration analysis. Phase-based motion estimation and magnification are utilized to experimentally determine the resonance frequencies and operational deflection shapes and identify the Young’s and shear moduli of gelatinous hydrogel structures. The experimental results of this study provides promising potential of implementing the proposed approach for applications in areas including advanced manufacturing and soil characteristics identification.
Bistable vibration energy harvesters have been used to achieve strong energy harvesting performance over a wide frequency bandwidth. Performance of bistable energy harvesters is dependent on whether the external excitation is large enough to surpass the minimum threshold to high energy, or ‘snap through’ oscillations. Studies have indicated that lowering the potential energy barrier via an auxiliary unit is an effective way to ensure that high energy orbits are achieved. Recent advancements have shown that directly extracting energy from an auxiliary unit used to dynamically lower the potential barrier of a bistable energy harvester can enhance performance. However, there remains an unexplored opportunity for further improvement by incorporating nonlinearity into the auxiliary harvesting element. Thus, to advance the state of the art, this research introduces an energy harvesting system composed of a bistable cantilever harvester magnetically coupled to an auxiliary nonlinear harvesting element. An analysis of the system potential energy indicates that the additional nonlinear characteristics of the coupled harvesting element can enable tailoring of the potential energy profile such that quad-stability, or multi-directional bistability, can be achieved. Investigation of the quasi-static potential energy trajectory of the proposed device indicates that the number of stable states, height of the potential energy barrier, and snap through amplitude may all be tailored through consideration of the effective linear stiffness of the nonlinear harvesting unit. Numerical simulations of the system dynamics indicate that the additional nonlinearity incorporated into the coupled system improves broadband harvesting performance.
Noise is unavoidable and ever-present in measurements. As a result, signal denoising is a necessity for many scientific and engineering disciplines. In particular, structural health monitoring applications aim to detect often weak anomaly responses generated by incipient damage (such as acoustic emission signals) from background noise that contaminates the signals. Among various approaches, stochastic resonance has been widely studied and adopted for denoising and weak signal detection to enhance the reliability of structural heath monitoring. On the other hand, many of the advancements have been focused on detecting useful information from the frequency domain generally in a postprocessing environment, such as identifying damage-induced frequency changes that become more prominent by utilizing stochastic resonance in bistable systems, rather than recovering the original time domain responses. In this study, a new adaptive signal conditioning strategy is presented for on-line signal denoising and recovery, via utilizing the stochastic resonance in a bistable circuit sensor. The input amplitude to the bistable system is adaptively adjusted to favorably activate the stochastic resonance based on the noise level of the given signal, which is one of the few quantities that can be readily assessed from noise contaminated signals in practical situations. Numerical investigations conducted by employing a theoretical model of a double-well Duffing analog circuit demonstrate the operational principle and confirm the denoising performance of the new method. This study exemplifies the promising potential of implementing the new denoising strategy for enhancing on-line acoustic emission-based structural health monitoring.
KEYWORDS: Transducers, Inductance, System integration, Signal to noise ratio, Data modeling, Scanning electron microscopy, Mathematical modeling, Inverse problems, Finite element methods, Damage detection
The piezoelectric impedance-based method for damage detection has been explored extensively for its high sensitivity to
small-sized damages with low-cost measurement circuit which enables remote damage monitoring. While the method
has good potential, the amount of feasible impedance data is usually much less than the number of required system
parameters to accurately identify the damage location/severity via an inverse formulation. This data incompleteness
forms a highly underdetermined problem and because of this numerical ill-conditioning, the predicted damage
parameters will be significantly influenced by unavoidable measurement noise and the accuracy of the base-line model.
In this study, the state of the art of impedance-based damage identification is advanced by incorporating a tunable
piezoelectric circuitry with the structure to enrich the impedance measurements. This piezoelectric circuitry introduces
additional degrees of freedom to the structure and changes the dynamics of the coupled system. By tuning the inductance
value, it is possible to perform various measurements under different system dynamics which reflects the damage effect.
Therefore, if performed systematically, notably increased sets of measurement can be obtained, which will improve the
inverse problem to be less underdetermined. Clearly, we can expect the accuracy and robustness in damage identification
to be significantly enhanced. Numerical case study on localizing damage in a fixed-fixed beam using spectral element
method is performed to demonstrate the effectiveness of the new method for structural damage identification.