Accurate measurement of the dynamic parameters of viscoelastic materials is crucial for vibration and acoustic performance prediction. However, inverse parameter identification via Frequency Response Functions (FRFs) faces increasing difficulties at higher frequencies, primarily due to the inherent numerical instability of classical modal-function-based FRF models, which restricts their use to low-order modes. This study presents an enhanced inverse identification method for determining the high-frequency-dependent shear modulus and loss factor of viscoelastic sandwich beams. The core contribution is an improved FRF model, where the conventional divergent modal function is replaced by a stable, Fourier-series-based analytical formulation. This key modification overcomes computational limitations, enabling stable and accurate high-frequency parameter identification. The method integrates this advanced model with an optimization algorithm and is validated through multi-point impact tests, demonstrating a significant extension of the identifiable frequency range with maintained efficiency. This work provides a stable, accurate, and practical tool for high-frequency inverse characterization of viscoelastic materials, essential for advanced damping design.

Accurate measurement of viscoelastic materials dynamic parameters is critical for viscoelastic behavior characterization and vibro-acoustic performance, yet remains challenging due to their inherent frequency-dependent properties. This study develops an enhanced simplex algorithm to identify frequency-dependent shear modulus and loss factor in viscoelastic-cored aluminum sandwich beams, incorporating a restart mechanism for improved convergence, multi-point experimental validation through frequency response functions, and comparative evaluation of four objective functions. Results demonstrate enhanced high-frequency identification accuracy, particularly with MSE-based objectives in multi-parameter scenarios. Compared with the traditional method, it has certain advantages in high frequency calculation accuracy and efficiency, which proves the reliability of this method. This approach provides a reliable framework for viscoelastic characterization, demonstrating practical value in engineering applications.

Diffuse acoustic fields are widely employed for material testing in industry, and loudspeaker-array synthesis offers high accuracy and flexible control. However, prior work has focused primarily on linear or planar arrays, with little attention to curved geometries. In this study, we introduce an arcuate loudspeaker array for diffuse acoustic field synthesis and compare its performance to that of a one-dimensional linear array across three metrics: synthesis accuracy, transfer-matrix condition number, and energy concentration within a prescribed sector. Loudspeaker directivity is modeled as frequency-dependent: at low frequencies each unit behaves as an ideal monopole (omnidirectional), while at mid frequencies an acoustic-center-compensated source model captures weak directivity. To explain the observed differences, we analyze wave-front matching via Huygens' principle and spectral concentration through spatial wavenumber decomposition. Numerical simulations demonstrate that the arcuate array outperforms the linear array in mid-to-high frequency bands, achieving both lower synthesis error and higher sector energy concentration. These findings are validated experimentally in an anechoic chamber using eight-unit linear and arcuate arrays. Our results show that a curved array geometry more effectively focuses diffuse energy into the target region, reducing leakage and enabling high-precision field synthesis.

In the automotive, aerospace, and architectural acoustics fields, diffuse acoustic fields are employed to assess sound transmission through various materials. In the context of diffuse acoustic field synthesis using loudspeaker arrays, the conventional approach relies on establishing control points within the target area and measuring transfer functions between the loudspeakers and these points to optimize driving signals. This approach, however, guarantees accuracy only at the control points and results in increased errors elsewhere, while also requiring extensive transfer-function measurements. To enhance the global synthesis accuracy and reduce the measurement effort, we propose a statistical pressure-matching method enhanced by control-point augmentation with transfer-function interpolation. The proposed method increases the density of refined control points within the target region and employs a loudspeaker acoustic model to interpolate transfer functions throughout the entire synthesis area. The loudspeaker’s sound radiation is modeled as distinct pairs of monopole sources, positioned at segmented acoustic centers based on amplitude attenuation and phase delay. Experimental validation conducted with a 1D loudspeaker array in an anechoic chamber demonstrates that this approach achieves improved synthesis accuracy across the entire target region compared to traditional control-point-based methods, while substantially reducing the required number of transfer-function measurements.

This paper introduces a novel time-frequency signal analysis method for analyzing nonlinear and non-stationary signals called the Reduced-order Time-Frequency Transform (RTFT). The RTFT technique offers the capabilities of traditional time-frequency transformations by employing Pearson's Correlation Coefficient to selectively reduce the data volume in the joint time-frequency domain. This method emphasizes highly correlated frequencies and phases leading to a more efficient data representation without significant loss of accuracy. The RTFT is validated through comparative analysis with established methods, including the Short-Time Fourier Transform (STFT), Hilbert-Huang Transform (HHT), Fourier Synchrosqueezed Transform (FSST), and Wavelet Synchrosqueezed Transform (WSST). Several non-stationary synthesized and real-world vibration-based condition monitoring signals are analyzed using the RTFT and these mentioned methods to demonstrate the superiority of the RTFT in reducing data volume while maintaining accuracy.

Reducing vibration and noise in fluid-filled pipeline systems is critical for enhancing the acoustic stealth of underwater vehicles. However, uncertainties inherent in the complex vibro-acoustic response and transmission of these systems render traditional deterministic methods inadequate. To address this, this paper proposes a hybrid methodology, named ISM-PCE, combining the impedance synthesis method (ISM) and polynomial chaos expansion (PCE), to efficiently estimate the low-order statistical moments of the frequency response function (FRF) of pipelines, validated by experiments and numerical simulations on homogeneous straight pipes. Results show that the normal ISM-PCE accurately estimates the mean FRF under single dimensional parameter (pipe inner diameter) uncertainty, but its variance estimation accuracy is insufficient in the resonance frequency band. Therefore, a stochastic frequency transformation method was introduced, significantly improving variance estimation accuracy and enabling successful multiple dimensional parameters uncertainty analysis. The results demonstrate that the normal ISM-PCE and its improved variant provide an efficient and accurate methodology for uncertainty quantification of vibration responses in fluid-filled pipeline systems. Although only fluid-filled straight pipes have been analyzed in this paper, the proposed methodology is generalizable and applicable to more complex fluid-filled pipeline systems.

Since the advent of the Industry 4.0 era, the demand for intelligent industrial machining that emphasizes high precision and optimized operational costs has risen significantly. Signal processing methodologies offer promising solutions to enhance condition monitoring challenges. However, achieving more accurate methods and effective data volume management remains an open problem. One promising method is the Reduced-order Symptom Recognition (RSR) technique, which helps track the condition of tools by analyzing time-frequency spectrums. This is particularly useful for studying signals that change over time, like the vibrations from bearings that are wearing out. However, the RSR method still has some areas that need improvement, such as accuracy in both time and frequency analysis and its ability to manage data volume effectively. To address these issues, we have developed an Enhanced Reduced-order Symptom Recognition (ERSR) method. This approach uses Pearson's correlation coefficient to make the time-frequency representations clearer. We tested the ERSR method using the NASA-FEMTO bearing dataset, which includes signals from normal operation to failure. By comparing our method with other established time-frequency techniques, we have showed that the ERSR method not only provides accurate spectra but also helps control the amount of data effectively. This research introduces the Enhanced Reduced-order Symptom Recognition (ERSR) method, which aims to provide clearer joint time-frequency spectrums while minimizing data volume without sacrificing accuracy. The effectiveness of this method has been confirmed through comparisons with established techniques, making it useful for practical condition monitoring applications, particularly for diagnosing bearing faults.

Experimental Modal Analysis (EMA) plays a crucial role in understanding the dynamic responses of structures to vibration by extracting their modal parameters such as natural frequencies and modal damping and vibration modes. These parameters are essential for assessing structural performance and identifying potential vulnerabilities. As the construction industry embraces sustainable materials, Cross-laminated Timber (CLT) has become a sustainable alternative to traditional materials like reinforced concrete and steel. However, the inherent variability of wood, resulting from factors such as growth conditions, fibre structure, and moisture content, introduces significant fluctuations in the dynamic response of CLT. This variability presents challenges in the broader application of CLT in construction. Despite its increasing use in multistory buildings, a comprehensive assessment of its vibrational characteristics remains incomplete. This study addresses this gap by identifying the dispersion in CLT's transfer functions and modal parameters through EMA. A CLT slab was divided into 24 nominally identical beam-like substructures, composed of outer layers of Norway spruce and a middle layer of Scots pine. EMA was performed in a broad frequency spectrum along three principal directions, revealing notable variability in resonance frequencies, modal damping, and vibration transfer functions. The study also examines the distinct characteristics of the bending, torsional, and axial vibration modes, providing deeper insights into the variability between the different modes. The findings of this article contribute to a more refined understanding of the dynamic properties of CLT and their associated variability.

To address the issue of low-frequency micro-vibrations affecting the performance of large precision instruments, this paper proposes a hybrid active-passive vibration isolation system. While air springs are widely elected as excellent passive isolation elements, their nonlinear characteristics and resonance limit their effectiveness in isolating low-frequency vibrations. To enhance the vibration isolation performance, piezoelectric actuators are serially connected to the air springs. Theoretical modeling of the air spring stiffness and natural frequency is conducted, followed by a further analysis of its transmissibility using the multiple scales method. Due to the nonlinear nature of the vibration isolation system, a minimum variance self-tuning control is employed to adjust controller parameters in real-time. To validate the isolation performance of the hybrid isolator, tests and experiments are conducted. The experimental results demonstrate that the hybrid isolator exhibits good control effectiveness against both single-frequency and random disturbances near resonance frequencies, affirming its practicality and efficacy. This system holds promising application prospects in vibration isolation for experimental tables, ships, and large-scale equipment.

Synthesizing random pressure fields with loudspeaker arrays in a laboratory setting requires acquiring a global transfer matrix of all channels between the loudspeaker array and the microphone array. This inevitably involves measuring a large and unwieldy number of transfer functions. Therefore, we propose a prediction method for the transfer matrix under free-field conditions, combining a substantially reduced number of measurements with specific predictions based on segmented acoustic centers. In free-field conditions, if only three sets of transfer functions are measured for each loudspeaker and the remaining entries in the global transfer matrix are predicted using analytical expressions, the results show that the normalized error between the predicted and measured transfer matrices can be less than -13 dB The experimental results indicate that, based on a one-dimensional loudspeaker array in a standard anechoic chamber, this prediction method shows promise for accurately reproducing random pressure fields, such as a diffuse acoustic field and the pressure field in the spanwise direction of a turbulent boundary layer. Additionally, the prediction method demonstrates the potential for extension to two-dimensional synthesis.

Through the advancement of Data Science methodologies, a new era in output-only identification techniques has been inaugurated, driven by the integration of data-driven methodologies within the realm of Structural Health Monitoring (SHM). This study endeavors to introduce a simplified data-driven approach catering to System Identification (SI) and Response Estimation (RE). This is realized through the utilization of a summation of sine functions, fashioned as a model to harmonize with time domain vibration and acoustic responses. The fidelity of the findings is subsequently authenticated through the application of the Frequency Domain Decomposition (FDD) technique. In addition to the identification process, the proposed approach extends its applicability to predicting time domain responses at novel locations. This augmentation is achieved by harnessing an enhanced methodology founded on the principles of the Dynamic Mode Decomposition (DMD) technique. The veracity of these predicted outcomes is underscored through a comparison with measurements recorded at the same locations, alongside concurrent analysis of DMD-derived results. In order to affirm the efficacy of the proposed methodology, a case study involving a building grappling with enigmatic vibration issues is meticulously selected. The findings underscore that the proposed technique not only adeptly discerns unidentified vibrations without resorting to frequency domain transformation techniques, but also facilitates precise estimation of time domain responses.

Finite Element (FE) model updating is crucial for identifying key parameters in structural design and improving predictive accuracy. Despite extensive research on advanced FE procedures approved for user applications, persistent disparities remain in real-world scenarios, especially for complex materials like wood. Capturing accurate mechanical characteristics with traditional models poses challenges in sustainability projects. This study introduces a derivative-free model updating procedure using a Single-Objective Optimisation (SOO) incorporating observed and predicted natural frequencies and vibration modes. The objective function optimises tuning parameters to minimise discrepancies between predicted and observed outcomes. The focus is on Cross-laminated Timber (CLT), a composite wooden structure gaining traction as a sustainable alternative to materials like reinforced concrete and steel. However, the mechanical properties of CLT can vary due to inherent variability in wood's mechanical characteristics. This research identifies sensitive mechanical properties — longitudinal Young's modulus, internal shear moduli, and rolling shear modulus of CLT — using a model updating procedure based on a comprehensive set of data from Experimental Modal Analysis (EMA). The study provides mathematical algebraic derivations of the updating procedure and a step-by-step implementation algorithm to facilitate practical application in structural engineering.

A boundary element model for the transmission of vibration over the surface of multi-layered ground has been developed.The model represents two dimensional layers over an elastic half-space.A harmonic load acts over a nite width on the surface.For layers of uniform thickness the results are compared with those from an exact semi-analytical model.Experimental evidence is presented on the thickness of layers and the imponance of this parameter in determining the propagation characteristics.The boundary element model allows the solution of problems with non-uniform layers.An example showing the effects of sloping layers is given.

Extensive studies have been conducted on joint time-frequency transforming techniques for non-stationary time series with the aim of enhancing both time and frequency accuracies and resolutions. These techniques have been successfully applied in various research areas. However, controlling and minimizing computational costs and data volume by reducing complexities have remained open challenges in this case. This need is becoming increasingly crucial in condition monitoring using Cloud-Edge computing, where these areas face a relatively high volume of data to process. Therefore, this paper introduces an innovative approach called the Reduced-order Time-Frequency Transform (RTFT) technique, which utilizes Pearson's Correlation Coefficient to effectively decrease the amount of transformed data and, consequently, the data volume in the joint time-frequency domain. Validation of the RTFT technique involves the implementation and comparison to well-established methods such as Short-Time Fourier Transform (STFT), Hilbert-Huang Transform (HHT), Fourier Synchrosqueezed Transform (FSST), and the Wavelet Synchrosqueezed Transform (WSST). Comparative analysis of the results demonstrates that the proposed RTFT technique yields an acceptable level of accuracy while significantly reducing the data volume.

This paper introduces an efficient computational procedure for analyzing the propagation of harmonic waves in layered elastic media. This offers several advantages, including the ability to handle arbitrary frequencies, depths, and the number of layers above an elastic half-space, and efforts to follow dispersion curves and flag up possible singularities are investigated. While there are inherent limitations in terms of computational accuracy and capacity, this methodology is straightforward to implement for studying free or forced vibrations and obtaining relevant response data. We present computations of wavenumber dispersion diagrams, phase velocity plots, and response data in both the frequency and time domains. These computational results are provided for two example cases: plane strain and axisymmetry. Our methodology is grounded in a well-conditioned dynamic stiffness approach specifically tailored for deep-layered strata analysis. We introduce an innovative method for efficiently computing wavenumber dispersion curves. By tracking the slope of these curves, users can effectively manage continuation parameters. We illustrate this technique through numerical evidence of a layer resonance in a real-life case study characterized by a fold in the dispersion curves. Furthermore, this framework is particularly advantageous for engineers addressing problems related to ground-borne vibrations. It enables the analysis of phenomena such as zero group velocity (ZGV), where a singularity occurs, both in the frequency and time domains, shedding light on the unique characteristics of such cases. Given the reduced dimension of the problem, this formulation can considerably aid geophysicists and engineers in areas such as MASW or SASW techniques.

The application of Machine Learning methodologies has been particularly noteworthy and abundant in pattern and symptom recognition across various research areas. However, Tool Condition Monitoring remains a chal-lenging subject due to the gradual wearing out of cutting tools during the machining process. Such failure leads to reduced accuracy and quality of the machined surface of the workpiece, resulting in increased costs. This research proposes an innovative ML-based method to clarify failure symptoms of cutting tools in the frequency and time-frequency domains. The study involves five cutting tools as experimental case studies during a 200 -minute machining operation. The results are validated using the Fast Fourier Transform, Short-time Fourier Transform, Empirical Mode Decomposition, and Variational Mode Decomposition methods, to demonstrate that the suggested methodology better identifies failure symptoms compared to other mentioned methods. One advantage of the proposed method is that considering a lower order of the system results in clearer frequency and time-frequency domain diagrams without sacrificing accuracy.

In this study, we investigate the feasibility of using a vibration-based metric for predicting low-frequency structure-borne noise in cross-laminated timber (CLT) buildings. The overall aim here is to facilitate the conceptual design for CLT buildings in terms of estimating noise levels. Since noise levels are inherently sensitive to architectural or mechanical design changes, in the low frequency regime, the problem of predicting these as a metric is well-known as a "high in computational cost" but "low in confidence solution" problem. Here, a reduced and robust prediction metric offers a way forward in estimating intrusive noise levels while capturing the effect of conceptual design changes. The degree of correlation between the vibration-based metric and the predicted noise levels was investigated by performing linear regression on datasets generated from a parameterised finite element model of a CLT building structure. A generally high degree of correlation between the vibration-based metric and the noise levels is observed. The effect of employing various frequency bands and sets of vibration evaluation points are investigated, and it is concluded that a high degree of correlation is obtained using only 3 × 3 vibration evaluation points per CLT panel.

There are well-known methods for determining natural frequencies and mode shapes of displacement for layered elastic media of finite depth with classical stress-free or rigid boundary conditions. There are also methods for handling more complex problems with specific boundary conditions, such as several finite layers resting over a half-space. However, these existing methods either allow evaluation of only the first few modes or require complex back-propagation analyses to achieve stability. There is currently no straightforward explicitly determined method that can determine the natural frequencies and mode shapes for all frequencies and layer depths. This paper introduces an alternative approach to address this limitation by strategically writing the dynamic stiffness matrices of attached layers. The main advantages of this strategy for the modeler include the ability to consider arbitrary frequencies, layer depths, and the number of layer strata over an elastic half-space. Importantly, there is no need to sub-divide layers, which is a requirement in many other methods. While there are some limitations in terms of computational accuracy and capacity, this methodology remains straightforward to program and compute relevant response outputs for general studies of free or forced vibration. The paper provides explicit entries for the involved matrices and presents computations of wavenumber dispersion diagrams, phase velocity plots, and response data in both the frequency and time-domains. Two case studies in earthquake assessments, one for plane-strain and another for axisymmetry, demonstrate the effectiveness of the methodology. The approach is based on a well-conditioned dynamic stiffness method, specifically developed for this purpose, which allows for the study of deep-layered strata. The computational efficiency of the method allows for fast computations even on regular desktop or laptop computers, with response analysis taking only tenths of a second for a forced response analysis. Numerical evidence of a layer resonance, resulting from the presence of a ZGV (zero group velocity) mode phenomenon, is demonstrated through a case study of a ground profile with layers hundreds of meters in depth. Solutions in both the frequency and time-domains highlight this special case.

Ground vibration generated by rail and road traffic is a major source of environmental noise and vibration pollution in the low-frequency range. A promising and cost-effective mitigation method could be the use of heavy masses placed as an array on the ground surface near the road or track, these could be concrete or stone blocks, or specially designed brick walls, for example. This work concerns the effectiveness of such “blocking” masses. We consider propagating waves in a finite depth elastic layer assuming plane strain conditions. Given that the masses are considered solid objects we may place these on the surface, embedded or submerged in the elastic medium where a finite element method is considered as a computational solution technique. Next, we may assume the masses are aligned as a periodic infinite array and enforce periodic boundary conditions around a “unit-cell”. By consideration of propagating waves via Floquet-Bloch theory we shall investigate the existence or lack thereof of propagating surface and body waves. This analysis supports a semi-analytical lumped-parameter method assuming the blocking masses are point masses situated on an elastic waveuide. The work is enhanced by an example highlighting advantages and disadvantages of multiple-mass scatterers in terms of possible stopband intervals related to propagating surface waves.

In this paper, the necessity of modelling the enclosed air in room volumes of buildings made of cross-laminated timber, with respect to the prediction of vibration transmission, is investigated. Coupled structural-acoustic finite element analysis is employed for an example building structure. It is found that modelling the air in the receiver room, i.e. in the room where the vibration response is evaluated, has a marked effect on the vibration transmission between panels.