The transition from light internal combustion engine (ICE) vehicles, such as cars and vans, to light electric vehicles (EVs) presents an opportunity to reduce road traffic noise exposure in urban environments, which still needs to be quantified. Although the noise emissions of light ICE vehicles are generally well understood, including during acceleration and deceleration, the noise emitted by light EVs has so far not been studied as thoroughly, in particular during acceleration. This study thus proposes a correction model for the noise emissions of light EVs during acceleration, based on pass-by measurements under reference conditions. Data were collected for 6 vehicle models at both steady speed and full acceleration. The difference in noise levels between these two conditions was analysed to develop the correction model. This correction model accounts for both speed and acceleration at an octave-band level. The resulting model shows that acceleration has no impact on the noise emissions of light EVs in the 63 and 125 Hz octave bands, and that acceleration may increase the overall A-weighted emissions of a light EV by up to 5 dBA, at 20 km/h. Furthermore, the analysis suggests that deceleration does not increase noise emissions for light EVs. This contribution paves the way for the integration of EV-specific noise emissions into noise exposure assessment frameworks, enabling a more comprehensive understanding of the potential benefits associated with the transition towards EVs.

Wave-based structural identification for real honeycomb sandwich structures has become an important research focus. However, most existing wave-based identification methods suffers from experimental uncertainties and a limited frequency range of applicability. To this end, we present a new wave-based structural identification framework, which includes two promising material identification methods – linear and nonlinear – suitable for honeycomb sandwich structures. The advantages of the identification process are reflected on two aspects: Firstly, the Algebraic Wavenumber Identification (AWI) technique reliably extracts complex wavenumbers over a wide frequency range under stochastic conditions, serving as input for the identification process. Secondly, a novel frequency-dependent, stepwise estimation strategy is proposed for honeycomb sandwich structures, greatly enhancing the precision of material parameter determination. Noteworthy, the proposed structural identifications enable the recovery of both equivalent dynamic and static mechanical properties. The experimental applications on a real beam, plate, and shell are presented. Key results show that (1) The proposed stepwise strategy reduces the relative error of wavenumbers of the tested beam to below 3.5%, improving parameter accuracy and ensuring estimation success; (2) For the tested plate, the estimated Young's modulus of skins, shear modulus of the core, and dynamic Hooke's matrix demonstrate satisfied precision; (3) It is the first to extract mechanical parameters of real curved structures using wave-based propagation parameters.

Electric vehicles (EVs) are expected to reduce urban noise pollution due to their quieter operation at low speeds. However, research has shown that EVs produce significant additional noise during acceleration, even at low speeds, primarily due to tyre–road interaction under high torque loads. This study investigates the impact of acceleration noise on two key exposure indicators—the equivalent continuous sound level (LAeq) and the number of noise events—using microscopic traffic simulations coupled with noise emission and propagation models. Acceleration noise was modelled using correction terms for both internal combustion engine (ICE) vehicles and EVs, based on recent literature. Simulations were performed on a real-world microscopic traffic model of Tartu (Estonia), and the fraction of EVs in the fleet was varied. Results show that neglecting acceleration noise can lead to underestimating LAeq by up to 4.3 dBA and missing close to half of noise events, particularly in electric fleets. The influence of acceleration noise is more pronounced at louder locations and for high-exceedance noise events. These findings highlight the need to integrate acceleration noise into noise exposure assessments, especially as EV penetration increases in urban traffic.

Accurate real material modeling is essential for structural dynamic analysis and design. Reliable structural parameters estimation, involving geometric and material parameters, is a key prerequisite, yet many existing methods primarily address homogenized material properties, which is inadequate for multilayer composites with complex geometrical core. To this end, this paper introduces a robust wave-based approach to structural parameter identification of individual layers, using only full-field displacement data. Specifically, the Algebraic K-Space Identification 2D technique (AKSI 2D) initially extracts wavenumber space (k-space) from measured structural responses, while surrogate optimization subsequently aligns this experimental k-space with the Wave Finite Element Method (WFEM)-derived numerical k-space to estimate structural parameters. The superiority of the proposed identification method stems from: (1) the ability of the AKSI 2D to automatically and accurately identify wavenumbers in any wave propagation direction from displacement fields on 2D grids, even in noisy environments, eliminating the need for complex filtering and specific point layouts; (2) the capacity of the WFEM in modeling wave propagation within multilayer structures with complex geometries, using unit cell-based operations within finite element software; and (3) the efficiency of the surrogate optimization in solving high-dimensional problems by finding the global minimum with high computational efficiency. To validate the accuracy of the proposed method, the structural parameters of each layer in two numerical cases, a four-layer laminated carbon fiber panel and a kelvin cell-based sandwich composite panel, are estimated. The inverted structural parameters show good agreement with the reference values, with an averaged relative error of less than 3.5%, even when a high level of white noise is added to the simulated displacement field. In addition, the structural parameters of a real parallelogram core sandwich panel is updated experimentally. These studies confirm that the proposed approach aligns with the intuitive decision-making of structural engineers for material characterization and modeling, offering adaptability for diverse structural design tasks.

This paper proposes an inverse method to characterize wave and energy propagation in curved structures, addressing the challenges of accurately obtaining dispersion curves, wavenumber space, and damping loss factors caused by their complex dynamics. The proposed method, Generalized Algebraic K-Space Identification (GAKSI) technique, is developed within the algebraic identification framework, enables the extraction of complex wavenumbers of multidimensional signals from full-field measured maps for the first time. By introducing iterated integrals and multivariate Laplace transform, the method can effectively filter signal noise, enhancing the accuracy of extracted wave propagation parameters. In this paper, the proposed method is applied to isotropic open shells with different geometric parameters and a real honeycomb cylindrical shell. Extracted results are compared with those from the reference methods. An in-depth analysis compares the characterization of shells and plates under varying signal noise levels. The findings demonstrate that the proposed method achieves high precision even under noisy conditions: the relative error for the extracted wavenumber converges to around 2.5% when the signal-to-noise ratio (SNR) exceeds 5, while the relative error for the extracted damping loss factor converges to approximately 5.5% when the SNR exceeds 10. Furthermore, the observations reveal that curvature-induced bending-membrane coupling enhances the damping properties, with this effect becoming more pronounced as the wave propagation direction transitions from the axial to the circumferential direction. These findings validate the capability of proposed method to characterize dispersion and damping properties in curved structures, offering promising potential for further applications in structural analysis, such as structural optimization and design.

Reducing children's exposure to traffic-related noise and air pollution in urban areas is a critical challenge. Therefore, evaluating traffic management strategies in a computational environment offers a practical tool for planners, policymakers, and researchers. However, a key research gap remains: most studies evaluate traffic strategies on air pollution, noise, or traffic separately. Few quantify their combined impacts in a single framework. To address this, we propose a multi-criteria evaluation approach and apply it by comparing four scenarios against a baseline. Through a comprehensive panel of statistical, spatial, and temporal analyses of traffic conditions, air pollutant concentrations, and noise levels, we find that: (1) restricting vehicle access during student arrival times significantly reduces exposure to both noise and air pollution; and (2) speed limit reductions have only limited effects on noise and may, under certain conditions, increase air pollution levels.

The full model parameters estimation of heterogeneous multilayer composites (HMC), involving geometric parameters and static-dynamic viscoelastic properties, has attracted considerable attention for both damage diagnosis and the design of new materials. However, this remains a challenge in current research due to the complexity involved in identifying special layers. To this end, we developed a robust wave-based method to estimate the structural parameters of each layer in HMCs using full-field displacement data. The method follows a two-stage inversion process. In Stage I, it estimates geometric and elastic parameters, and in Stage II, it determines damping properties. These parameters can be static, dynamic, linear, nonlinear, or mixed. The objective is to optimize the identification process by combining the multi-scale wave and energy propagation modeling and characterization numerical methodology that automatically incorporates the limited knowledge on both the used predicted Finite Element model (whatever its complexity) and experimental data (inevitably noisy). The Condensed Wave Finite Element Method with Contour Integral solver (CWFEM-CI) is proposed to model wave and energy propagation in mesoscopic predicted models by solving a nonlinear eigenvalue problem. It enables complex wavenumber extraction in arbitrary directions while reducing computational cost through model order reduction approach, Component Mode Synthesis (CMS). At the macroscopic scale, Algebraic K-Space Identification 2D (AKSI 2D) is applied to retrieve complex wavenumbers from real materials, serving as reference data for inverse optimization. By embedding iterated integrals into the mathematical foundation of the method, signal noise is effectively suppressed, thereby ensuring accurate material identification. Finally, the identification problem is formulated and solved iteratively using the surrogate optimizer, which minimizes the difference between predicted and experimental wave propagation parameters. The accuracy and effectiveness of the proposed method are validated through numerical experiments on linear elastic, nonlinear viscoelastic, and heterogeneous multilayer models, using both synthetic and real full-field data.

The assessment of the exposure to road traffic noise pollution and of associated health conditions is usually based on energy-average noise levels. However, the number of noise events to which an individual is exposed has proven essential to the prediction of annoyance and sleep disturbance. Unfortunately, no standard method has been adopted for the counting of noise events. To address this shortcoming, Brown and De Coensel designed, in 2018, a generalised algorithm for the detection of road traffic noise events. The authors evaluated the performance of this algorithm for multiple sets of input parameters, but the setup employed for this testing was simplistic. The present study thus aims to benchmark the proposed parameter sets for the noise event detection algorithm in a controlled but realistic environment, consisting of a calibrated microscopic traffic simulation in the entire city of Tartu, Estonia, which includes interrupted traffic conditions and urban infrastructure. The performance assessment of a parameter set is shown to be highly dependent on context, i.e., location and time of day, making definitive, universally applicable conclusions unrealistic. Rather, this study enables comprehensive insights that guide the selection of adapted parameter sets for various traffic situations, including the number of parameter sets, suitable detection thresholds, and recommended time gaps to implement.

This study proposes the determination of a log-linear regression model for estimating average traffic flow rates using a single measured noise indicator. This model was trained and tested with noise and traffic count data collected over 400 days at a case study location in central Stockholm, Sweden. Through a comprehensive analysis of the correlation between various noise indicators and traffic counts, the best performing indicator was selected, enabling traffic flow estimation with an average day-wise RMSE of 2.31 vehicles per minute and percentage error of 7%. Different measurement campaign strategies were tested to assess their effectiveness in providing reliable training data, demonstrating that campaigns measuring over all hours of the day and all days of the week perform significantly better than campaigns restricted to typical weekday working hours. This study highlights the potential of noise-based traffic estimation as a complementary, cost-effective approach for enhancing real-time traffic monitoring and transportation assessment.

In exterior acoustic simulations with the finite element method, accurately modeling an infinite domain using a finite computational space is challenging due to reflections at the truncated boundaries. This study introduces an m-th order operator for implementing absorbing boundary conditions that releases the geometrical constraints and minimizes reflections. Furthermore, the resulting finite element problem is naturally well-suited for a range of reduced-order models, such as the moment-matching, projection based well-conditioned asymptotic waveform evaluation (WCAWE), allowing efficient frequency sweep studies in large models. Our combined approach significantly enhances simulation efficiency, allowing for extensive frequency analysis with minimal domain size without compromising accuracy. However, the combination of higher order formulations of the considered absorbing boundary condition with the WCAWE approach also exhibits limitations in accuracy for a given size of reduced basis. This is aspect is the object of ongoing investigations.