Search Results (54)

Magnetorheological elastomers (MREs) exhibit magnetic-field-dependent stiffness and can, therefore, be used to tune the natural frequency of a dynamic vibration absorber through the applied coil current. This study evaluates the extension of a previously developed MRE-based semi-active absorber to two automotive noise, vibration, and harshness transmission paths: road-input-related subframe vibration and engine-induced seat vibration. A standard cylindrical actuator was installed below the subframe of a passenger vehicle and tested on a rough road at 10, 20, and 30 km/h. Vehicle speed was used as the operating-condition feedback variable for current selection, while additional fixed-current measurements were conducted to characterize the current-dependent response. Cabin sound pressure was measured simultaneously. A compact actuator with a lightweight resin housing was also developed for the seat application; engine speed was used as the feedback variable, and the current was selected with reference to the second-order engine excitation. The natural frequencies of both actuators increased with current, although the compact actuator had a smaller tuning range. The actuator-installed conditions produced local, current-dependent reductions in subframe and seat vibration spectra. Changes in cabin sound pressure were smaller, frequency-dependent, and not uniform. The results demonstrate the feasibility of the operating-condition-based tuning of MRE dynamic absorbers for local automotive vibration paths, while also identifying limitations associated with passive installation effects, magnetic-circuit efficiency, packaging, and multi-path cabin acoustics. Full article

The larvae of wood-boring insects often cause damage to wooden buildings, furniture, and objects of cultural heritage. A fundamental practical challenge in assessment and conservation is reliably distinguishing between active and inactive infestations—a distinction that conventional visual inspection methods cannot always resolve with confidence. We present a selection of experiments for the implementation of acoustic emission (AE) measurement methods for these inspections, spanning from highly controlled laboratory settings to practical field tests. An overview explains the physical principles of structure-borne sound emission by feeding larvae and the AE sensors and other instrumentation used (AMSY-6 stationary system and the single-channel Insect Activity Detection System, IADS). Laboratory experiments with standardized small specimens containing single larvae of Hylotrupes bajulus are described in detail, examining the influence of temperature, sensor coupling method, and sensor-to-larva distance on detection reliability. Verification experiments on large structural timber with unknown infestation levels demonstrated the practical applicability of the method and enabled qualitative localization of larval activity. Field applications in historic buildings, churches, and cultural heritage objects are presented and discussed. The results confirm that AE sensors can reliably detect larval activity of H. bajulus in standardized specimens at signal-to-noise ratios clearly distinguishable from negative controls. Sensor sensitivity decreases significantly with distance, with reliable detection up to approximately 30 cm under the tested conditions. Although interpretive expertise in wood biology, infestation assessment, and conservation expertise remains essential, the results indicate that AE sensors can indeed be used to detect larval activity inside wood in a non-destructive way in a broad range of field and laboratory applications and across a range of conditions. Full article

This paper presents up-to-date technical solutions for mitigating vibration and structure-borne noise in buildings, caused mainly by urban infrastructure. It is intended to draw attention to the problem of noise and vibration pollution, against which people and buildings should be protected rationally and properly according to the concept of sustainable development as it relates to the architecture, engineering and construction (AEC) sector (one common sector). It reviews different passive methods of protection against vibration impacts, such as those provided by the elastic support of structures, emphasizing their advantages and limitations. The application of optional solutions using suitable elastomeric materials is presented through examples of various special-purpose buildings designed and built in recent years. The implemented mitigation measures are described in detail and briefly evaluated in the context of ensuring effective vibration isolation that complies with stringent requirements related to the accepted level of vibro-acoustic influence in the considered facilities. The paper provides an overview of the practice-oriented application of an elastic support technique in the design and realization of buildings, which enhances the vibro-acoustic comfort of their occupants and users. The general aspects of sustainability in the context of types of elastomeric vibro-isolating materials are also discussed. Full article

Taking a metro over-track TOD (Transit-Oriented Development) project in Chongqing as the engineering background, this study adopts a combined research approach integrating field measurements and numerical simulation. A coupled finite element model of the train–track–tunnel–soil–building system and a regional acoustic model are established to systematically reveal the vibration response characteristics of building clusters above the depot induced by metro operation, the propagation mechanism of structure-borne secondary noise, and the distribution patterns of the regional acoustic environment, while identifying the areas where vibration and noise exceed the prescribed limits as well as the key influencing factors. On this basis, following a hierarchical mitigation strategy consisting of source control, path interruption, and receiver protection, an integrated control scheme is proposed through the coordinated application of track vibration reduction, building vibration isolation, acoustic environment optimization, and building sound insulation. The engineering applicability and control effectiveness of the proposed scheme are further verified by numerical simulation. The findings of this study can provide theoretical support and technical reference for the refined design and integrated prevention and control of vibration and acoustic environments in similar metro over-track development projects. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

This study proposes a noise-robust unsupervised acoustic anomaly detection framework for early identification of abnormal operating conditions in rotating machinery under submarine-like environments with severe data scarcity. In such environments, underwater background noise and onboard interference sources significantly degrade signal quality, while limited computing resources constrain the deployment of high-complexity deep learning models. To address the lack of labeled fault data, the publicly available MIMII dataset was adopted as a proxy platform, and representative submarine interference sources were physically modeled, including colored background noise, structure-borne resonance, band-limited auxiliary noise, tonal components, and sensor noise. These components were combined and scaled to predefined SNR levels (−6 to 6 dB) to generate realistic noise-augmented data. Three unsupervised approaches were compared under edge deployment constraints: (i) Gaussian Mixture Model (GMM) with statistical MFCC features, (ii) statistical-feature-based Ensemble Autoencoder, and (iii) Conv1D-based Ensemble Autoencoder using 1-s log Mel-spectrogram segments. Performance was evaluated in terms of AUC, F1-score, and computational cost. Results show that GMM provides competitive detection performance with minimal computational burden, whereas Conv1D achieves superior accuracy when temporal fault patterns dominate, at the expense of higher complexity. The study provides practical design guidelines for acoustic anomaly detection under multi-noise and resource-constrained conditions. Full article

Vibration and noise generated by rail transit systems pose significant constraints on their environmental sustainability. Although extensive research has been conducted by scholars on vibration and noise in rail transit, quantitative studies specifically investigating the influence of train speed on the vibration and noise of elevated rail transit are scarce. Therefore, this study selected a typical elevated section of Wuhan Metro Line 21 and systematically performed field tests to measure the vibration and noise induced by trains passing at speeds of 20, 40, 60 and 80 km·h⁻¹. Based on the test results, the vibration characteristics of the rails, track slab, and bridge structure, as well as the radiation characteristics of wheel–rail noise and bridge structure-borne noise under different speeds, were investigated. The study further explored the impact of train speed variation on the vibration and noise of the elevated rail transit system. The results indicate that the vibration acceleration levels of both the outer and inner rails increase significantly with train speed. Each time the speed doubles, the vibration level rises by approximately 11.5 dB for the outer rail and 10.0 dB for the inner rail. The vibration of the track slab and bridge structure is notably lower than that of the rails. Each time the speed doubles, the vibration acceleration level at various measurement points increases by an average of about 8.5–9.0 dB. Wheel–rail noise is primarily concentrated in the frequency bands around 630 Hz and 3150 Hz. Each time the speed doubles, the trackside noise level increases by an average of approximately 7.2–7.6 dB(A). Noise measured under the bridge shows a distinct peak around 100 Hz, which aligns with the vibration frequency of the bottom slab. Due to the shielding effect of shrubs, noise in the 63–100 Hz frequency band is attenuated at measurement points above ground level. Each time the speed doubles, bridge structure-borne noise increases by about 4.5–5.0 dB(A), representing a lower growth rate compared to wheel–rail noise. The findings of this research are expected to contribute to vibration and noise reduction strategies and support the sustainable development of rail transit systems. Full article

To systematically investigate the influence of metro train types on the operational noise of elevated rail transit, this study conducted field measurements on elevated sections of the Wuhan Metro Yangluo Line, Wuhan Metro Line 2, and Guangzhou Metro Line 4, comparing the noise characteristics of 4-car A-type, 6-car B-type, and 4-car L-type trains operating at 70 ± 2 km/h. Analysis of sound pressure levels and frequency spectra at multiple points revealed that wheel-rail noise peaks occurred at 630 Hz and 2500 Hz for A-type trains, around 800 Hz for B-type trains, and within 800–1250 Hz for L-type trains, while bridge structure-borne noise was consistently concentrated in the 63–100 Hz low-frequency range. Distinct emission patterns were observed: at on-girder points, noise levels were highest for A-type trains, followed by B-type and then L-type trains, a trend potentially linked to axle loads; conversely, at under-girder points, the order reversed with L-type trains producing the highest noise. At points 7.5 m and 25 m from the track centerline, A-type and B-type trains exhibited similar noise levels, whereas L-type trains were slightly quieter. Furthermore, all three train types showed a consistent noise attenuation rate of approximately 6 dB(A) per doubling of distance from the track centerline. The findings will serve as a reference and basis for rail transit noise prediction and control. Full article

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by which cross-sectional asymmetry influences the vibro-acoustic coupling of viaducts. A 2.5D Hybrid Finite Element-Boundary Element Method (FEM-BEM) was employed to model a parametric box girder under eccentric track loading, and the numerical framework was validated against analytical benchmarks. The “Modal Symmetry Index” (MSI) and “Acoustic Asymmetry Indicator” (AAI) were defined to evaluate the effects of the asymmetry parameter ($\alpha$) on sound field distribution. Numerical results reveal a nonlinear “V-shaped” relationship between geometric asymmetry and acoustic directivity. While severe asymmetry ($\alpha >0.15$) exacerbates noise deflection via flexural–torsional coupling, a critical “self-balance zone” exists. Specifically, moderate asymmetry ($\alpha \approx 0.07$) effectively neutralizes load eccentricity, reducing the AAI from 1.5 dB (in strictly symmetric designs) to nearly 0 dB. Robustness analysis under right-side loading conditions further confirms a “reverse deflection” phenomenon, verifying that the proposed self-balance design minimizes directional sensitivity. These findings challenge the traditional assumption that geometric symmetry is acoustically optimal. A “competition–compensation” mechanism is identified, suggesting that deliberate, slight geometric asymmetry can serve as an effective passive noise control strategy for viaducts. Full article

Accurate and reliable extraction of building footprints from LiDAR point clouds is a fundamental task in remote sensing and urban scene reconstruction. Building footprints serve as essential geospatial products that support GIS database updating, land-use monitoring, disaster management, and digital twin development. Traditional image-based methods enable large-scale mapping but suffer from 2D perspective limitations and radiometric distortions, while airborne or vehicle-borne LiDAR systems often face single-viewpoint constraints that lead to incomplete or fragmented footprints. Recently, backpack mobile laser scanning (MLS) has emerged as a flexible platform for capturing dense urban geometry at the pedestrian level. However, the high noise, point sparsity, and structural complexity of MLS data make reliable footprints delineation particularly challenging. To address these issues, this study proposes a Deep Line-Segment Detection–Driven Building Footprints Extraction Framework that integrates multi-layer accumulated occupancy mapping, deep geometric feature learning, and structure-aware regularization. The accumulated occupancy maps aggregate stable wall features from multiple height slices to enhance contour continuity and suppress random noise. A deep line-segment detector is then employed to extract robust geometric cues from noisy projections, achieving accurate edge localization and reduced false responses. Finally, a structural chain-based completion and redundancy filtering strategy repairs fragmented contours and removes spurious lines, ensuring coherent and topologically consistent footprints reconstruction. Extensive experiments conducted on two campus scenes containing 102 buildings demonstrate that the proposed method achieves superior performance with an average Precision of 95.7%, Recall of 92.2%, F1-score of 93.9%, and IoU of 88.6%, outperforming existing baseline approaches by 4.5–7.8% in F1-score. These results highlight the strong potential of backpack LiDAR point clouds, when combined with deep line-segment detection and structural reasoning, to complement traditional remote sensing imagery and provide a reliable pathway for large-scale urban scene reconstruction and geospatial interpretation. Full article

Ground-borne vibrations caused by railway traffic represent a significant environmental concern, particularly in densely populated or vibration-sensitive urban areas. These phenomena can lead to discomfort and annoyance among residents, interfere with the operation of sensitive equipment, and even threaten the integrity of heritage sites or structurally vulnerable buildings and infrastructures. Building on these concerns, this paper presents a comprehensive review of the current state of knowledge on the subject. It begins by examining the impacts of ground-borne vibrations on both people and structures, followed by an overview of the regulatory frameworks implemented in different countries to manage these effects, with a focus on four examples from Europe and North America. The review then systematically explores the key factors associated with the generation and propagation of ground-borne noise and vibrations. Furthermore, prediction methodologies are categorised into four groups—analytical and semi-analytical, numerical, empirical and AI-based models—and critically assessed. Finally, the paper reviews mitigation strategies applied at the source, along the propagation path, and at the receiver, assessing their effectiveness in reducing the identified impacts. Full article

This study provides a comprehensive review of advancements in the field of train–track–subway system interaction dynamics and suggests future directions for research and development. Mathematical modeling of train–track–subway interaction system is addressed, including wheel–track contact mechanics and wear, train multibody dynamics, train–track system coupling dynamics, track slab subsystem dynamics, subway tunnel–ground interaction models, building vibration excited by ground-borne seismic waves, and noise. Advanced computing and simulation techniques used for numerical studies of the dynamics of train–track–subway system interaction in the past two decades are also addressed, including high-performance computing with efficient algorithms, multi-physics and multi-scale simulation, real-time hardware-in-the-loop simulation, and laboratory and field validation. The study extends the applications of train–track–subway interaction dynamics to subway route planning, structural and material design, subway maintenance, operations safety and reliability, and passenger comfort. Emerging technologies and future perspectives are also reviewed and discussed, including artificial intelligence, smart sensing and real-time monitoring, digital twin technology, and sustainable design integration. Full article

During the suspension flight of high-altitude scientific balloons in near-space, they are highly vulnerable to time-varying wind field disturbances, which tend to excite multiple distinctive torsional modes of the balloons themselves, thereby interfering with the observations of balloon-borne equipment. Focusing on the azimuth control of the balloon-borne gondola, this paper designs a simplified decoupling mechanism and a reaction wheel as actuators. Specifically, the reaction wheel achieves azimuth tracking through angular momentum exchange, while the simplified decoupling mechanism performs the functions of decoupling and unloading. To fully utilize the control performance of the actuating structure, this paper further proposes a control algorithm based on a nonlinear differential tracker and neural network PID. Simulation results demonstrate that under typical wind disturbances and sensor noise conditions, the proposed system exhibits excellent smoothness and high-precision and stable control performance. This research provides a significant basis for stable observation platforms in precise near-space observation missions. Full article

Electrification introduces additional NVH (noise, vibration and harshness) challenges during the development of powertrain mounting systems due to high-frequency excitations from the powertrain and the absence of masking effects from the combustion engine. In these frequency ranges, engine mounts can stiffen up to a factor of five due to continuum resonances, reducing their structure-borne sound isolation properties and negatively impacting the customer’s NVH perception. Common hardening factors used during elastomer mount development are therefore limited in terms of their applicable validation frequency range. This study presents a methodology for determining decoupled permissible stiffness ranges for a double-isolated mounting system up to 1500 Hz, based on solution space engineering. Instead of optimizing for a single best design, we seek to maximize solution boxes, resulting in robust stiffness ranges that ensure the fulfillment of the formulated system requirements. These ranges serve as NVH requirements at the component level, derived from the sound pressure level at the seat location. They provide tailored guidelines for mount development, such as geometric design or optimal resonance placement, while simultaneously offering maximum flexibility by spanning the solution space. The integration of machine learning approaches enables the application of large-scale finite-element models within the framework of solution space analysis by reducing the computational time by a factor of $7.19·{10}^3$. From a design process standpoint, this facilitates frontloading by accelerating the evaluation phase as suppliers can directly benchmark their mounting concepts against the permissible ranges and immediately verify compliance with the defined targets. Full article

In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms. Full article