Vol. 59 No. 2 (2025)

Open Access

Article

Article ID: 2372

Innovative intelligent and expert system of bridges damage identification via wavelet packet energy curvature difference method integrated with artificial intelligence algorithms
by Wael A. Altabey

Sound & Vibration, Vol.59, No.2, 2025;

DOI: https://doi.org/10.59400/sv2372

Bridges are important infrastructure for highways. Monitoring their status is of great significance to ensure safe operations. In this work, a novel integrated technique from wavelet packet energy curvature difference (WPECD) and artificial intelligence (AI) for bridge damage identification is established. Initially, the damages are simulated in the bridge decks by changing the material stiffness reduction levels of bridge elements by three levels (5%, 10%, 15%) to study the effect of damage on the bridge response. Then the WPECD maps are plotted from vibration response before and after damage to the bridge for each stiffness reduction level. Unfortunately, given the nonlinearity of damage geometry, it is not easily feasible to use WPECD maps for damage identification accurately. Therefore, the (WPECD) maps are used for training a new architecture of recurrent neural networks with long short-term memory blocks (RNN-LSTM) for bridge damage identification by predicting the wavelet functions and wavelet decomposition layer effect of each node in the bridge. The effectiveness and reliability of the proposed approach were confirmed by numerical and experimental results. The performance of the proposed technique achieved high scores of accuracy, regression, and F-score equal to 93.58%, 90.43% and 88.17% respectively indicating the applicability of the proposed method for use on other important highway infrastructure.

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Open Access

Article

Article ID: 2358

Scrutinizing highly nonlinear oscillators using He’s frequency formula
by Gamal M. Ismail, Galal M. Moatimid, Ibrahim Alraddadi, Stylianos V. Kontomaris

Sound & Vibration, Vol.59, No.2, 2025;

DOI: https://doi.org/10.59400/sv2358

Highly nonlinear oscillators are examined in their capacity to simulate intricate systems in engineering, physics, biology, and finance, as well as their diverse behavior, rendering them essential in the development of resilient systems and technological advancement. Therefore, the fundamental purpose of the current work is to analyze He’s frequency formula (HFF) to get theoretical explanations of many types of very nonlinear oscillators. We investigate, in both analytical and computational, the relationship between elastic forces and the solution of a specific oscillator. This oscillator exhibits significant nonlinear damping. It is assumed that the required quantity of trigonometric functions matches the solution of a strong nonlinear ordinary differential equation (ODE) that explains the motion. The novel approach definitely takes less processing time and is less complex than the traditional perturbation methods that were widely used in this field. This novel method, which is essentially giving a linearization of the nonlinear ODE, is known as the non-perturbative approach (NPA). This procedure produces a new frequency that is similar to a linear ODE, much as in a fundamental harmonic scenario. Readers will benefit from an in-depth account of the NPA. The theoretical findings are validated by numerical examination using Mathematical Software (MS). The theoretical and numerical solution (NS) tests yielded fairly similar findings. It is a well-established principle that classical perturbation methods trust on Taylor expansions to approximate restoring forces, therefore simplifying the current situation. When the NPA is used, this vulnerability does not present. Furthermore, the NPA enables a thorough assessment of the problems’ stability analysis, which was a not possible using prior conventional methodology. Consequently, the NPA is a more appropriate responsibility tool for examining approximations in extremely nonlinear oscillators in MS.

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Open Access

Article

Article ID: 2025

Noise suppression of high-speed cavity treated with leading and trailing edge spoilers
by Yang Liu , Dongping Yin, Dangguo Yang, Yong Luo, Fangqi Zhou, Bin Dong, Ronghui Ning, Chunhui Yan

Sound & Vibration, Vol.59, No.2, 2025;

DOI: https://doi.org/10.59400/sv2025

High-speed cavity flow and the induced noise have been continuously investigated in the aerospace industry. They may not only influence the performance of instruments inside the cavity, but also cause fatigue damage to the structures, which threaten the safety of aircraft. Therefore, cavity noise suppression is practically important. In this work, the leading edge sawtooth, the leading edge cylinder, and the trailing edge contouring are employed to suppress high-speed cavity noise at Mach numbers of 2.0, 2.5, 3.0, 3.5, and 4.0. Wind tunnel tests were performed to study the influence of the control parameters associated with these suppression methods. The results show that the leading edge sawtooth and cylinder are able to effectively suppress cavity noise at Ma = 2.0, 2.5, but prove ineffective at Ma = 3.0, 3.5, and 4.0, suggesting that the critical Mach number locates between 2.5 and 3.0. Above the critical Mach number, cavity noise would increase. In comparison, the noise suppression effect of the trailing edge contouring is relatively minor, and it shows a monotone decreasing trend as Mach number increases from 2.0 to 4.0.

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Open Access

Article

Article ID: 2939

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Sound & Vibration, Vol.59, No.2, 2025;

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Open Access

Article

Article ID: 2935

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Sound & Vibration, Vol.59, No.2, 2025;

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Open Access

Editorial

Article ID: 2627

Vibration: A bibliometric analysis
by João Paulo Davim

Sound & Vibration, Vol.59, No.2, 2025;

DOI: https://doi.org/10.59400/sv2627

Vibration is a mechanical phenomenon in which oscillations occur around an equilibrium point. The Scopus database was used for the bibliometric analysis, based on the term {vibration}. The better result shows in the function of the number of documents produced: year 2024; source Proceedings of SPIE; author Inman, D.J.; affiliation Ministry of Education of China; country China; document type article; scientific area Engineering and funding support National Natural Science Foundation of China.

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Editor-in-Chief

Prof. Jun Yang

Institute of Acoustics, Chinese Academy of Sciences, China

ISSN

1541-0161 (Print)

2693-1443 (Online)

Publication Frequency

Bi-monthly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

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Volume Arrangement

Vol 59 Iss 2 (2025)

Vol 59 Iss 1 (2025)

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Featured Articles

Soundscapes in Arab Cities: A Systematic Review and Research Agenda

In the context of Arab cities, this study explores the intricate interplay between cultural, historical, and environmental elements that shape their unique soundscapes. The paper aims to shed light on this underrepresented field of study by employing a three-fold research approach: systematic review, a comprehensive literature review, and the formulation of a future research agenda. The first part of the investigation focuses on research productivity in the Arab world regarding soundscape studies. An analysis of publication trends reveals that soundscape research in Arab cities is still an emerging area of interest. Critical gaps in the existing body of literature are identified, highlighting the importance of addressing these gaps within the broader context of global soundscape research. The second part of the study delves into the distinctive features that inform the soundscapes of Arab cities. These features are categorized into three overarching groups: (i) cultural and religious life, (ii) daily life, and (iii) heritage and history, by exploring these factors, the study aims to elucidate the multifaceted nature of Arab urban soundscapes. From the resonating calls to prayer and the vibrant ambiance of traditional cafes to the bustling markets and architectural characteristics, each factor contributes to the auditory tapestry that defines Arab cities. The paper concludes with a forward-looking research agenda, proposing sixteen key questions organized into descriptive and comparative categories. These questions emphasize the need for a more profound understanding of sound perception, sources, and the impact of urban morphology on the soundscape. Additionally, they highlight the need for interdisciplinary research, involving fields such as urban planning, architecture, psychology, sociology, and cultural studies to unravel the complexity of Arab urban soundscapes.

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multi-objective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path. This approach involves introducing a 1D-Convolutional Neural Network (1D-CNN) model for predicting the sound insulation performance of the floor acoustic package, thereby avoiding the limitations of conventional CAE approaches that rely solely on “data-driven” methods. The proposed method was applied and tested using specific vehicle models, and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression (SVR) models.

A Sound Quality Evaluation Method for Vehicle Interior Noise Based on Auditory Loudness Model

When designing and optimizing the hull of vehicles, their sound quality needs to be considered, which greatly depends on the psychoacoustic parameters. However, the traditional psychoacoustic calculation method does not consider the influence of the real human ear anatomic structure, even the loudness which is most related to the auditory periphery. In order to introduce the real physiological structure of the human ear into the evaluation of vehicle sound quality, this paper first carried out the vehicle internal noise test to obtain the experimental samples. Then, the physiological loudness was predicted based on an established human ear physiological model, and the noise evaluation vector was constructed by combining the remaining four psychoacoustic parameters. Finally, the evaluation vector was fitted into the subjective evaluation results of vehicle interior noise by a deep neural network. The results show that our proposed method can estimate the human subjective perception of vehicle interior noise well.

Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling

Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In response to these considerations, this paper introduces an innovative approach for predicting vehicle road noise by integrating mechanism-driven and data-driven methodologies. Specifically, a series model is devised, amalgamating mechanism analysis with data-driven techniques to predict vehicle interior noise. The simulation results from dynamic models serve as inputs to the data-driven model, ultimately generating outputs through the utilization of the Long Short-Term Memory with Autoencoder (AE-LSTM) architecture. The study subsequently undertakes a comparative analysis between different dynamic models and data-driven models, thereby validating the efficacy of the proposed series vehicle road noise prediction model. This series model, encapsulating the rigid-flexible coupling dynamic model and AE-LSTM series model, not only demonstrates heightened computational efficiency but also attains superior prediction accuracy.