Pub Date : 2025-04-25DOI: 10.1016/j.ymssp.2025.112735
Iman Dabbaghchian , Thomas J. Matarazzo , Soheil Sadeghi Eshkevari , Liam Cronin , Shamim N. Pakzad
Recently, crowdsourced smartphone-vehicle trip (SVT) data has enabled cost-effective estimation of bridge modal frequencies and absolute mode shapes. SVT data includes acceleration and GPS measurements collected from smartphones within vehicles as they cross the bridge. The SVT data is inherently contaminated mostly with sensor noise, vehicle dynamics, and road profile uncertainties. These factors cause variability in the amount of embedded bridge dynamic information, thereby affecting the overall data quality. This study presents a novel method and metric to quantify the SVT data’s quality based on each trip’s impact on the identified aggregated mode shape. Then, a data-driven model is used to detect the quality parameter automatically using the convolutional neural network. The model is trained and tested on over 900 asynchronous SVT data collected from smartphones over the Cadore viaduct bridge in Italy. The results demonstrated that this method could improve the quality of an identified mode shape, increasing the modal assurance criterion of 0.8 in blind aggregation to 0.97 with model sorting and eliminating low-quality trips. Ensuring the quality control of crowdsourced data is crucial due to multiple noise sources, and discarding erroneous datasets can significantly improve dynamic characterization identification of the bridge.
{"title":"Optimizing bridge modal property estimation via quality-driven crowdsourced smartphone data selection","authors":"Iman Dabbaghchian , Thomas J. Matarazzo , Soheil Sadeghi Eshkevari , Liam Cronin , Shamim N. Pakzad","doi":"10.1016/j.ymssp.2025.112735","DOIUrl":"10.1016/j.ymssp.2025.112735","url":null,"abstract":"<div><div>Recently, crowdsourced smartphone-vehicle trip (SVT) data has enabled cost-effective estimation of bridge modal frequencies and absolute mode shapes. SVT data includes acceleration and GPS measurements collected from smartphones within vehicles as they cross the bridge. The SVT data is inherently contaminated mostly with sensor noise, vehicle dynamics, and road profile uncertainties. These factors cause variability in the amount of embedded bridge dynamic information, thereby affecting the overall data quality. This study presents a novel method and metric to quantify the SVT data’s quality based on each trip’s impact on the identified aggregated mode shape. Then, a data-driven model is used to detect the quality parameter automatically using the convolutional neural network. The model is trained and tested on over 900 asynchronous SVT data collected from smartphones over the Cadore viaduct bridge in Italy. The results demonstrated that this method could improve the quality of an identified mode shape, increasing the modal assurance criterion of 0.8 in blind aggregation to 0.97 with model sorting and eliminating low-quality trips. Ensuring the quality control of crowdsourced data is crucial due to multiple noise sources, and discarding erroneous datasets can significantly improve dynamic characterization identification of the bridge.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"233 ","pages":"Article 112735"},"PeriodicalIF":7.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deformation monitoring at cantilever ends of large-span prestressed concrete rigid-frame bridges is vital for ensuring structural safety during symmetrical cantilever casting operations. Traditional contact-based measurement techniques are typically time-consuming and labor-intensive, whereas noncontact vision-based methods offer significant benefits in terms of multipoint deformation measurement and cost-effectiveness . However, their implementation in complex construction environments presents challenges including susceptibility to object occlusion, illumination variations, and reduced detection accuracy for various shaped artificial targets. To address these limitations, this study proposes an enhanced vision-based deformation measurement methodology for large-span prestressed concrete rigid-frame bridges under construction scenarios including partial target occlusion. The proposed methodology initially employs the U2-net, a learning-based background segmentation network, is combined with an incremental image repair network to automatically detect and repair occluded images. Afterward, an enhanced target detection algorithm, which integrates the Convolutional Block Attention Module (CBAM) with the You Only Look Once (YOLO)v8 neural network, is utilized to simultaneously extract deformation data from multiple targets attached to the bridge. The robustness and efficacy of the proposed method have been thoroughly verified through field tests on a prestressed concrete rigid-frame bridge during the symmetrical cantilever casting process. The results demonstrate that our proposed method greatly minimizes deformation anomalies due to object occlusion and efficiently captures deformation from targets of various shapes, such as circular and chessboard patterns. This method demonstrates significant potential for accurately measuring multipoint deformations of large-scale bridges in complex construction environments, thereby providing essential data for bridge safety assessment and construction strategy decision-making.
{"title":"Noncontact vision-based deformation measurement of a large-span prestressed concrete rigid-frame bridge under object occlusion","authors":"Yongding Tian , Yuanyuan Huang , Junhao Zhang , Junhu Shao , Yulin Zhan","doi":"10.1016/j.ymssp.2025.112774","DOIUrl":"10.1016/j.ymssp.2025.112774","url":null,"abstract":"<div><div>Deformation monitoring at cantilever ends of large-span prestressed concrete rigid-frame bridges is vital for ensuring structural safety during symmetrical cantilever casting operations. Traditional contact-based measurement techniques are typically time-consuming and labor-intensive, whereas noncontact vision-based methods offer significant benefits in terms of multipoint deformation measurement and cost-effectiveness . However, their implementation in complex construction environments presents challenges including susceptibility to object occlusion, illumination variations, and reduced detection accuracy for various shaped artificial targets. To address these limitations, this study proposes an enhanced vision-based deformation measurement methodology for large-span prestressed concrete rigid-frame bridges under construction scenarios including partial target occlusion. The proposed methodology initially employs the U<sup>2</sup>-net, a learning-based background segmentation network, is combined with an incremental image repair network to automatically detect and repair occluded images. Afterward, an enhanced target detection algorithm, which integrates the Convolutional Block Attention Module (CBAM) with the You Only Look Once (YOLO)v8 neural network, is utilized to simultaneously extract deformation data from multiple targets attached to the bridge. The robustness and efficacy of the proposed method have been thoroughly verified through field tests on a prestressed concrete rigid-frame bridge during the symmetrical cantilever casting process<em>.</em> The results demonstrate that our proposed method greatly minimizes deformation anomalies due to object occlusion and efficiently captures deformation from targets of various shapes, such as circular and chessboard patterns. This method demonstrates significant potential for accurately measuring multipoint deformations of large-scale bridges in complex construction environments, thereby providing essential data for bridge safety assessment and construction strategy decision-making.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112774"},"PeriodicalIF":7.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-25DOI: 10.1016/j.ymssp.2025.112773
Zejun Zheng , Dongli Song , Weihua Zhang , Rui Chen , Chao Ma , Wang Cui , Xiao Xu
Envelope spectrum analysis based on bandpass filtering is one of effective methods for fault diagnosis of rolling bearings. Finding the optimal filtering frequency band is the key to extract the fault information and also a major long-standing challenge. The difficulty of selecting the optimal filtering frequency band increases when the signal-to-noise ratio (SNR) is low. In order to solve this problem, the amplitude probability distribution statistical feature of the bearing vibration acceleration envelope signal is utilized as the fitness function in this paper. An improved grey wolf optimization algorithm named multi-zone grey wolf optimization (MZGWO) algorithm is proposed to find an optimal filtering frequency band. Combined with the bandpass filter construction method, the optimal bandpass filter is established to extract the hidden fault information in the original signal. The proposed method is analyzed and verified by the simulation signals of the bearing dynamics model and the experiment signals of the fault bearings, and compared with several optimal filtering frequency band search methods. The analysis results show that the proposed method is effective and has advantages in the search for the optimal filtering frequency band.
{"title":"An optimal filtering frequency band search method based on MZGWO in rolling bearings fault diagnosis","authors":"Zejun Zheng , Dongli Song , Weihua Zhang , Rui Chen , Chao Ma , Wang Cui , Xiao Xu","doi":"10.1016/j.ymssp.2025.112773","DOIUrl":"10.1016/j.ymssp.2025.112773","url":null,"abstract":"<div><div>Envelope spectrum analysis based on bandpass filtering is one of effective methods for fault diagnosis of rolling bearings. Finding the optimal filtering frequency band is the key to extract the fault information and also a major long-standing challenge. The difficulty of selecting the optimal filtering frequency band increases when the signal-to-noise ratio (SNR) is low. In order to solve this problem, the amplitude probability distribution statistical feature of the bearing vibration acceleration envelope signal is utilized as the fitness function in this paper. An improved grey wolf optimization algorithm named multi-zone grey wolf optimization (MZGWO) algorithm is proposed to find an optimal filtering frequency band. Combined with the bandpass filter construction method, the optimal bandpass filter is established to extract the hidden fault information in the original signal. The proposed method is analyzed and verified by the simulation signals of the bearing dynamics model and the experiment signals of the fault bearings, and compared with several optimal filtering frequency band search methods. The analysis results show that the proposed method is effective and has advantages in the search for the optimal filtering frequency band.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112773"},"PeriodicalIF":7.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.ymssp.2025.112753
Liyuan Li , Wen Cai , Bin Li , Kaixiang Li
The eddy current dynamic absorber demonstrates great potential in the field of vibration control due to its inherent decoupling between stiffness and damping components, the flexible regulation of damping, and the generation of non-contact damping forces. However, the performance of the eddy current damper in aerospace applications has not yet been explored. Considering the unique vibration characteristics of aerospace systems, the eddy current dynamic absorber for aerospace applications must incorporate nonlinearity, be lightweight, and demonstrate effectiveness across a broad frequency band. In this study, the lightweight and miniaturized eddy current dynamic absorber with a double-annular-plate configuration is employed. After characterizing the nonlinear damping properties, an optimal design approach has been proposed and verified numerically. Further, the optimized eddy current dynamic absorbers are utilized to suppress the broadband vibration of the thin-walled panel. Both the simulation and experimental results show that the modal vibrations of the panel are well suppressed by the optimal eddy current dynamic absorbers, with the reduction of the frequency response peak larger than 15.3 dB and the reduction ratio of RMS larger than 79 % within 500 Hz. The eddy current dynamic absorber can be considered a promising alternative to traditional linear dynamic absorbers in aerospace engineering. Overall, the design approach presented in this study advances the optimal tuning of the nonlinear dynamic absorbers and offers valuable insights for their practical implementation in suppressing vibrations within aerospace engineering structures.
{"title":"Design method and application in panel vibration suppression of nonlinear eddy current dynamic absorbers","authors":"Liyuan Li , Wen Cai , Bin Li , Kaixiang Li","doi":"10.1016/j.ymssp.2025.112753","DOIUrl":"10.1016/j.ymssp.2025.112753","url":null,"abstract":"<div><div>The eddy current dynamic absorber demonstrates great potential in the field of vibration control due to its inherent decoupling between stiffness and damping components, the flexible regulation of damping, and the generation of non-contact damping forces. However, the performance of the eddy current damper in aerospace applications has not yet been explored. Considering the unique vibration characteristics of aerospace systems, the eddy current dynamic absorber for aerospace applications must incorporate nonlinearity, be lightweight, and demonstrate effectiveness across a broad frequency band. In this study, the lightweight and miniaturized eddy current dynamic absorber with a double-annular-plate configuration is employed. After characterizing the nonlinear damping properties, an optimal design approach has been proposed and verified numerically. Further, the optimized eddy current dynamic absorbers are utilized to suppress the broadband vibration of the thin-walled panel. Both the simulation and experimental results show that the modal vibrations of the panel are well suppressed by the optimal eddy current dynamic absorbers, with the reduction of the frequency response peak larger than 15.3 dB and the reduction ratio of RMS larger than 79 % within 500 Hz. The eddy current dynamic absorber can be considered a promising alternative to traditional linear dynamic absorbers in aerospace engineering. Overall, the design approach presented in this study advances the optimal tuning of the nonlinear dynamic absorbers and offers valuable insights for their practical implementation in suppressing vibrations within aerospace engineering structures.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112753"},"PeriodicalIF":7.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.ymssp.2025.112686
Kamal Kishor Prajapati, Anup Ghosh, Mira Mitra
Data-driven structural health monitoring is becoming popular for its easy application to complex structures. Machine learning or deep learning models are crucial for predicting damage in structures, but their performance depends on the size of the training dataset. Obtaining data can be time-consuming, expensive, and difficult. This study is on the development of a semi-supervised generative adversarial network (SGAN) model that can be trained on fewer samples. The SGAN model is trained and tested on a benchmark dataset from the openguidedwaves (OGW) platform (Moll et al., 2019). The OGW dataset is a collection of Lamb wave interactions with a fiber reinforced composite plate under pristine and damaged conditions. In this study, three SGAN models have been developed using different numbers of supervised training samples and their performance has been evaluated. The developed SGAN models are also compared to the most commonly used deep learning (DL) algorithm, convolutional neural network (CNN) based models, and ResNet autoencoder-based transfer learning models to provide a more comprehensive performance assessment. Compared to CNN models and ResNet autoencoder-based transfer learning (TL) models, SGAN models demonstrated remarkable generalization abilities in detecting damage within composite plates. The performance of all models (SGAN, CNN, and TL) improved as the number of supervised samples increased when compared to their previous versions with fewer supervised samples. When accuracy is used as the performance metric, the top-performing SGAN model outperformed the leading CNN model by 24.62% and the best transfer learning (TL) model by 10.96%.
{"title":"Semi-supervised generative adversarial network (SGAN) for damage detection in a composite plate using guided wave responses","authors":"Kamal Kishor Prajapati, Anup Ghosh, Mira Mitra","doi":"10.1016/j.ymssp.2025.112686","DOIUrl":"10.1016/j.ymssp.2025.112686","url":null,"abstract":"<div><div>Data-driven structural health monitoring is becoming popular for its easy application to complex structures. Machine learning or deep learning models are crucial for predicting damage in structures, but their performance depends on the size of the training dataset. Obtaining data can be time-consuming, expensive, and difficult. This study is on the development of a semi-supervised generative adversarial network (SGAN) model that can be trained on fewer samples. The SGAN model is trained and tested on a benchmark dataset from the openguidedwaves (OGW) platform (Moll et al., 2019). The OGW dataset is a collection of Lamb wave interactions with a fiber reinforced composite plate under pristine and damaged conditions. In this study, three SGAN models have been developed using different numbers of supervised training samples and their performance has been evaluated. The developed SGAN models are also compared to the most commonly used deep learning (DL) algorithm, convolutional neural network (CNN) based models, and ResNet autoencoder-based transfer learning models to provide a more comprehensive performance assessment. Compared to CNN models and ResNet autoencoder-based transfer learning (TL) models, SGAN models demonstrated remarkable generalization abilities in detecting damage within composite plates. The performance of all models (SGAN, CNN, and TL) improved as the number of supervised samples increased when compared to their previous versions with fewer supervised samples. When accuracy is used as the performance metric, the top-performing SGAN model outperformed the leading CNN model by 24.62% and the best transfer learning (TL) model by 10.96%.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112686"},"PeriodicalIF":7.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.ymssp.2025.112654
Yi Yang , Hongjun Xiang , Zhiwei Zhang , Xinyue Dai , Wenxin Yi , Tao Wang , Yixin Chen
This study provides the first comprehensive experimental validation of isolated high-energy orbits and broken bands in nonlinear piezoelectric energy harvesters (NPEHs), phenomena previously predicted only through numerical simulations. These high-energy orbits, emerging in bistable configurations, significantly enhance energy harvesting efficiency. The broken band phenomenon, observed in both monostable and bistable systems, offers practical guidance for optimizing resistance selection. Our experimental results confirm the reliability of our previously proposed analysis framework and deepen the understanding of NPEH dynamics. These findings lay a foundation for developing more efficient energy harvesting strategies and have significant implications for practical applications.
{"title":"Experimental validation of isolated high-energy orbits and broken bands in nonlinear piezoelectric energy harvesters","authors":"Yi Yang , Hongjun Xiang , Zhiwei Zhang , Xinyue Dai , Wenxin Yi , Tao Wang , Yixin Chen","doi":"10.1016/j.ymssp.2025.112654","DOIUrl":"10.1016/j.ymssp.2025.112654","url":null,"abstract":"<div><div>This study provides the first comprehensive experimental validation of isolated high-energy orbits and broken bands in nonlinear piezoelectric energy harvesters (NPEHs), phenomena previously predicted only through numerical simulations. These high-energy orbits, emerging in bistable configurations, significantly enhance energy harvesting efficiency. The broken band phenomenon, observed in both monostable and bistable systems, offers practical guidance for optimizing resistance selection. Our experimental results confirm the reliability of our previously proposed analysis framework and deepen the understanding of NPEH dynamics. These findings lay a foundation for developing more efficient energy harvesting strategies and have significant implications for practical applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112654"},"PeriodicalIF":7.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.ymssp.2025.112738
Xuefeng Li , Huina Mao , Peter Göransson , Mohamed Ichchou , Romain Rumpler
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.
{"title":"Accurate structural parameter identification of individual layers of complex multilayer composites for improved simulations using wave and finite element methodology","authors":"Xuefeng Li , Huina Mao , Peter Göransson , Mohamed Ichchou , Romain Rumpler","doi":"10.1016/j.ymssp.2025.112738","DOIUrl":"10.1016/j.ymssp.2025.112738","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112738"},"PeriodicalIF":7.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An electromechanical coupling model is a fundamental tool to predict accurately the energy harvested by piezoelectric structures. A viscoelastic model is proposed to analyze a piezoelectric glued laminated plate harvester. In accordance with the Kelvin-Voigt relation and the von Karman plate theory, nonlinear electromechanical coupling governing equations are derived from the Newton-Euler method and the Gauss law. The Galerkin method is applied to obtain the discretized equations for the vibration around the non-trivial static equilibrium configuration produced by the structural weights. The harmonic balance method is employed to determined approximately the voltage and acceleration amplitude-frequency response with convergence considerations. The resulting amplitude-frequency responses agree well with those obtained from experiments. To understand the viscoelasticity effects, the amplitude-frequency response curves predicted by the viscoelastic model are compared with those predicted by the elastic model. The results demonstrate that viscoelastic damping contributes more significantly at the higher order modes and under the larger excitations. The proposed viscoelastic model is used to examine the effects of different parameters such as the steel ring radius, the concentrated mass, and the load resistance on the output power.
{"title":"A viscoelastic plate model for a glued laminated circular plate in a piezoelectric energy harvester","authors":"Ying Meng , Sha Wei , Tian-Chen Yuan , Hu Ding , Li-Qun Chen","doi":"10.1016/j.ymssp.2025.112757","DOIUrl":"10.1016/j.ymssp.2025.112757","url":null,"abstract":"<div><div>An electromechanical coupling model is a fundamental tool to predict accurately the energy harvested by piezoelectric structures. A viscoelastic model is proposed to analyze a piezoelectric glued laminated plate harvester. In accordance with the Kelvin-Voigt relation and the von Karman plate theory, nonlinear electromechanical coupling governing equations are derived from the Newton-Euler method and the Gauss law. The Galerkin method is applied to obtain the discretized equations for the vibration around the non-trivial static equilibrium configuration produced by the structural weights. The harmonic balance method is employed to determined approximately the voltage and acceleration amplitude-frequency response with convergence considerations. The resulting amplitude-frequency responses agree well with those obtained from experiments. To understand the viscoelasticity effects, the amplitude-frequency response curves predicted by the viscoelastic model are compared with those predicted by the elastic model. The results demonstrate that viscoelastic damping contributes more significantly at the higher order modes and under the larger excitations. The proposed viscoelastic model is used to examine the effects of different parameters such as the steel ring radius, the concentrated mass, and the load resistance on the output power.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112757"},"PeriodicalIF":7.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.ymssp.2025.112766
Yihan Du , Dong Wang , Qiang Wan , Di Yuan , Xuanhua Fan
This paper proposed an uncertainty quantification strategy from the hysteresis characteristics of the metal-rubber isolator (MRI) element to the nonlinear vibration responses of the MRI system. An innovative phenomenological model was developed to accurately reproduce the hysteresis behavior of the MRI element, and the uncertain model parameters along with their interval distributions were determined through repeated periodic excitation tests. By combining the Chebyshev interval method (CIM) with the Cut-high dimensional model representation (Cut-HDMR), a high-efficiency uncertainty propagation analysis method, CIM-HDMR, was proposed to estimate the vibration response bounds of the MRI system under multidimensional interval uncertainties. The nonlinear vibration responses were obtained through the combination of the harmonic balance method and an alternate frequency/time procedure (AFT-HBM). Additionally, the determined interval parameters were used to construct a clustered parameter space based on sensitivity analysis, which further reduced the effects of overestimation and nonlinearities in traditional interval algorithms. Numerical results revealed a significant effect of uncertain hysteresis behavior on the vibration responses of the MRI system, manifesting as “resonance bands” and “frequency shifts.” The proposed strategy accurately quantified the interval distribution characteristics of vibration responses within the parameter space and elucidated the influence mechanisms of uncertain hysteresis behavior on the MRI system.
{"title":"Uncertainty modeling for metal-rubber isolator system and its propagation effects on nonlinear vibration responses","authors":"Yihan Du , Dong Wang , Qiang Wan , Di Yuan , Xuanhua Fan","doi":"10.1016/j.ymssp.2025.112766","DOIUrl":"10.1016/j.ymssp.2025.112766","url":null,"abstract":"<div><div>This paper proposed an uncertainty quantification strategy from the hysteresis characteristics of the metal-rubber isolator (MRI) element to the nonlinear vibration responses of the MRI system. An innovative phenomenological model was developed to accurately reproduce the hysteresis behavior of the MRI element, and the uncertain model parameters along with their interval distributions were determined through repeated periodic excitation tests. By combining the Chebyshev interval method (CIM) with the Cut-high dimensional model representation (Cut-HDMR), a high-efficiency uncertainty propagation analysis method, CIM-HDMR, was proposed to estimate the vibration response bounds of the MRI system under multidimensional interval uncertainties. The nonlinear vibration responses were obtained through the combination of the harmonic balance method and an alternate frequency/time procedure (AFT-HBM). Additionally, the determined interval parameters were used to construct a clustered parameter space based on sensitivity analysis, which further reduced the effects of overestimation and nonlinearities in traditional interval algorithms. Numerical results revealed a significant effect of uncertain hysteresis behavior on the vibration responses of the MRI system, manifesting as “resonance bands” and “frequency shifts.” The proposed strategy accurately quantified the interval distribution characteristics of vibration responses within the parameter space and elucidated the influence mechanisms of uncertain hysteresis behavior on the MRI system.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112766"},"PeriodicalIF":7.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.ymssp.2025.112768
Guangshuai Han , Yen-Fang Su , Cihang Huang , Na Lu , Yining Feng
In the realm of concrete strength monitoring, the fusion of piezoelectric sensor data with machine learning algorithms holds significant promise. However, previous studies have been limited by datasets that lack diversity, often confined to homogeneous mix designs and sensor types, which restrict the applicability of developed models in variable real-world conditions. This study addresses this gap by constructing a comprehensive database that encompasses an unprecedented variety of mix designs and sensor deployments, capturing a wide spectrum of concrete behaviors. Our research introduces a novel 1D Convolutional Neural Network (1DCNN) architecture paired with a baseline mechanism, specifically tailored for the analysis of electro-mechanical impedance (EMI) signals. Rigorous validation through train-test splitting has demonstrated the high accuracy of our model within laboratory settings, achieving an R2 value of 0.96 and a mean prediction error of 2.68 MPa for concrete strength. We’ve also conducted field validation tests on actual highway concrete pavement projects, marking a pioneering application of lab-trained models for strength prediction under field conditions. The successful application of our model in these field tests confirms its robustness and reliability, marking a pivotal step toward practical deployment. Our study not only bridges the gap between laboratory research and field application but also sets a new benchmark for dataset informativeness and model versatility in the domain of concrete strength prediction.
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