Structural Control & Health Monitoring最新文献

{"title":"Identification and Localization of Structural Damage Using the Second-Largest Eigenvalue of the Mutative-Scale Symbolic Matrix as the Damage Indicator","authors":"Shuang Meng,&nbsp;Dongsheng Li,&nbsp;Xiaoyu Bai","doi":"10.1155/stc/2484661","DOIUrl":"https://doi.org/10.1155/stc/2484661","url":null,"abstract":"<div>\u0000 <p>Time series–related methods in structural damage detection have gained increasing recognition due to their effectiveness, yet they face limitations in accuracy and efficiency for data processing, particularly in damage localization. In this study, we propose a novel method that utilizes a mutative-scale symbolic matrix, which extracts the second-largest eigenvalue as a damage indicator, to address the difficult problems of damage detection under random excitation. Unlike the conventional symbolized time series method, the mutative-scale symbolic matrix method selects data from the virtual impulse response function series at specific intervals, based on the Pearson correlation coefficient, and uses these data with the intervals to construct the mutative-scale symbolic matrix through joint occurrence entropy. The second-largest eigenvalue of the matrix is identified as an effective damage indicator which significantly magnifies the variations in structural characteristics. Damage localization is achieved by exploring damage occurrence between different reference and measurement points, and the flexibility in selecting these points enables a more precise determination of the damaged area according to the technology process based on dichotomy. A 10-DOF numerical model subjected to random Gaussian white noise is initially employed to validate the accuracy of the damage indicator for damage identification and localization. Subsequently, upon experimental application to a testbed structure, the proposed method exhibited super robustness in data selection under different damage types, with higher computational efficiency than conventional methods.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2484661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Vibration Isolation Platforms for Wafer Front Opening Unified Pod Transporting Carts Under Raised Floor Irregularities in Industrial Factories","authors":"Chien-Liang Lee,&nbsp;Yung-Tsang Chen,&nbsp;Yen-Po Wang,&nbsp;Lap-Loi Chung,&nbsp;Meng-Chieh Liu,&nbsp;Li-Yen Lu","doi":"10.1155/stc/2134915","DOIUrl":"https://doi.org/10.1155/stc/2134915","url":null,"abstract":"<div>\u0000 <p>This study was conducted to examine the vibration control performance of the active isolation platform (AIP) implemented on the cart table (CT) of a moving front opening unified pod (FOUP) transporting cart to prevent damage to fragile silicon wafers during transportation across different buildings in semiconductor fabs. Additionally, the equation of motion for the proposed AIP–cart system simulated by a full vehicle model under raised floor irregularities was derived. Moreover, the direct output feedback control algorithm was used to determine the optimal feedback gain matrix for calculating the active control forces of the AIP. Furthermore, the dynamic time histories of the proposed model under raised floor irregularities were analyzed by the discrete–time state–space procedure (SSP), and the numerical simulation results revealed that AIP effectively suppressed the bouncing (or vertical) acceleration with a reduction of &gt; 90% at FOUP locations to 2.37 m/s² (&lt; 9.81 m/s² or 1.0 g, the bouncing acceleration threshold) to prevent FOUPs (or fragile silicon wafers) from bouncing away from the CT without AIP, causing damages to the wafers via collisions. Moreover, AIP greatly reduced the pitching angular rotation with a reduction of &gt; 65% to prevent the sliding of FOUP-stored wafers from the supporting slots inside FOUPs when the FOUP-transporting cart traversed through a larger bump between the expansion joints. The flexible AIP that demanded less control force (27.08 N) significantly isolated the high-frequency response transmitted from the CT and effectively enhanced its damping ratio to suppress the resonance low-frequency response induced by intermittent perforated floor irregularities or bumps. From a practical point of view, the proposed AIP scheme implemented on CT can be adopted for protecting jumping- or sliding-induced collision damages to wafers (or similar fragile products) transported by carts to reduce huge economic losses in industry.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2134915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Nonlinear Output-Only Damage Detection Method Based on the Prediction Error of PCA Euclidean Distances Under Environmental and Operational Variations","authors":"Jiezhong Huang,&nbsp;Sijie Yuan,&nbsp;Dongsheng Li,&nbsp;Tao Jiang","doi":"10.1155/stc/4684985","DOIUrl":"https://doi.org/10.1155/stc/4684985","url":null,"abstract":"<div>\u0000 <p>Vibration-based damage detection relies on changes in structural dynamic features. However, environmental and operational variations (EOVs) can cause changes in dynamic features that mask those caused by damage. In addition, the EOV effects on dynamic features are often nonlinear, which limits the application of many linear damage detection methods. A novel nonlinear output-only method is proposed to address this. This method leverages variational mode decomposition (VMD) as a preprocessing step to remove seasonal patterns and noise from the modal frequencies. The first modes of the decomposition results (IMF1 signals) are then used to calculate the Euclidean distance based on the residual obtained by the principal component analysis (PCA) method. To eliminate the nonlinear EOV effects and provide normalized damage features for reliable continuous dynamic monitoring, a Gaussian process regression (GPR) model is trained to learn the underlying calculation rule of the PCA Euclidean distance. Due to the linear nature of PCA, the nonlinear EOV effects are still retained in both the PCA Euclidean distance and the GPR–predicted value. Through a subtraction process, their common nonlinear environmental effects can be removed, and the resulting prediction error can serve as a normalized feature sensitive to structural damage. The proposed method is validated through a simulated 7-DOF example and real data from the Z24 bridge, with several comparisons highlighting its effectiveness.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4684985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain Knowledge Embedded InSAR-Based 3D Displacement Monitoring of Urban Buildings","authors":"Ya-Nan Du,&nbsp;De-Cheng Feng,&nbsp;Gang Wu","doi":"10.1155/stc/8864614","DOIUrl":"https://doi.org/10.1155/stc/8864614","url":null,"abstract":"<div>\u0000 <p>Continuous monitoring of building displacement is crucial for urban structural safety. While traditional methods are costly, Interferometric Synthetic Aperture Radar (InSAR) offers a cost-effective alternative, providing long-term displacement data. However, due to the insensitivity of SAR radar to north-south displacement, using InSAR alone can only measure settlement and east-west displacement. To address this limitation, this paper presents a three-dimensional (3D) deformation extraction model. The model embeds domain knowledge to introduce additional constraints, which are then used to establish the relationship between north-south and east-west displacement. This relationship allows for the extraction of 3D displacement of buildings from the line of sight (LOS) displacement measured by InSAR. This model was applied to Tower 2 of Yingli International Financial Center (YIFC) in Chongqing, China, and the 3D displacement of the building between 2018 and 2021 was obtained.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8864614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of Bridge Girder Cumulative Displacement for Component Operational Warning Using Bayesian Neural Networks","authors":"Xiaoming Lei,&nbsp;Zhen Sun,&nbsp;Ao Wang,&nbsp;Tong Guo,&nbsp;Tomonori Nagayama","doi":"10.1155/stc/9974584","DOIUrl":"https://doi.org/10.1155/stc/9974584","url":null,"abstract":"<div>\u0000 <p>The main girders of suspension bridges experience significant deformation due to temperature variations, wind dynamics, and vehicle loads, causing movement at the girder ends and friction among components such as bearings, expansion joints, and viscous dampers. Early warning of the component anomaly is vital for preventive maintenance. This paper develops a two-stage framework for predicting girder end displacement to facilitate anomaly detection. First, a Bayesian neural network is employed to predict girder end cumulative displacement, accounting for uncertainties inherent in the prediction process. Second, an anomaly detection algorithm utilizing a Mahalanobis distance–based approach is implemented to provide warnings to operations based on both measured and predicted data. The effectiveness of the proposed approach is validated using data collected from multiple loads and displacement responses of a suspension bridge. The analysis reveals that the GEV distribution is highly proficient in capturing the underlying pattern of the cumulative displacement indicator, enabling the establishment of an appropriate threshold. This method proves successful in identifying anomalies in critical components such as viscous dampers, enhancing predictive and preventive maintenance practices and contributing to the longevity and safety of bridge infrastructure.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9974584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Deep Learning to Estimate Vibration Comfort of Large-Scale Shake Table During Operation","authors":"Minte Zhang,&nbsp;Tong Guo,&nbsp;Yueran Zong,&nbsp;Weijie Xu,&nbsp;Chee Kiong Soh","doi":"10.1155/stc/6888254","DOIUrl":"https://doi.org/10.1155/stc/6888254","url":null,"abstract":"<div>\u0000 <p>Shake tables are useful earthquake simulation tools for structural seismic experiment, but they may also inadvertently induce vibrations to nearby buildings while in operation. Accelerating the comfort level quantification process of these vibrations before conducting a shake table test is necessary. To this end, this paper focuses on the influence of vibration introduced by a 6 × 9 m large-scale shake table at Southeast University and presents a one-dimensional convolutional neural network–based deep learning approach to efficiently estimate the vibration comfort of the shake table laboratory and surrounding buildings. Based on the on-site structural vibration monitoring of shake table test, a three-dimensional numerical model of the shake table–soil–surrounding building system is established and validated through the finite element method, and thus a dataset comprising 12,215 groups of input (i.e., peak acceleration values and time-history of the triaxial ground motion) and output (i.e., three-directional acceleration response for nine measuring points of surrounding buildings) data is simulated. Thereafter, the deep learning network is trained with 80% of the dataset and tested with the remaining 20%. The test results indicate that the approach enables the network to directly extract dynamic features from triaxial ground motion accelerations and to accurately estimate the weighted acceleration level (WAL) of nine different locations at the surrounding buildings. Finally, the optimized network is verified through an actual shake table experimental test on a self-centering concrete structure, which confirms the superior performance of the proposed approach on shake table–induced vibration comfort estimation. The approach is also beneficial for researchers to design reasonable loading scenarios before conducting shake table tests.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6888254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring-Based Evaluation of Wind-Induced Vibration and Travel Comfort of Long-Span Suspension Bridge","authors":"Zhongxiang Liu,&nbsp;Haojun Cai,&nbsp;Tong Guo,&nbsp;Xingwang Liu,&nbsp;Yongtao Bai,&nbsp;Chunxu Qu","doi":"10.1155/stc/9962003","DOIUrl":"https://doi.org/10.1155/stc/9962003","url":null,"abstract":"<div>\u0000 <p>In this paper, evaluation of wind-induced vibration and travel comfort of the long-span suspension bridge were comprehensively conducted based on multisource monitoring data. The wind field distribution and turbulent characteristics during the normal and vortex-induced vibration (VIV) period were comparatively revealed. It reveals that the bridge experienced vertical VIV due to the long-duration wind with specific speed perpendicularly acting on the girder, which cannot be predicted by the turbulence intensity and gust factor. Meanwhile, dynamic response evolution, VIV lock-in effect, modal identification, and wavelet spectrum were further explored based on displacement and acceleration. The VIV frequency was consistent with a natural frequency of the bridge, whose mode can been determined by the deflection correlation heat map. The VIV was due to periodic vortex shedding generate aerodynamic forces, and the reaction of the structure vibration on vortex shedding can cause the vortex shedding frequency to be “locked” over a considerable range of wind speeds. According to driving visual safety and vibration tolerance, it is indicated that such VIV of the bridge may lead to the very discomfort for driving and pedestrian can tolerate short-term vibration in this period. Comfort evaluation for the bridge during the VIV should be further improved accuracy and reliability, which can contribute to emergency response to VIV situations. Note that a certain degree of discomfort may occur under normal vibration conditions, which raises doubts about the reasonableness of the limit value.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9962003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition of Structural Components and Surface Damage Using Regularization-Based Continual Learning","authors":"Yung-I Chang,&nbsp;Rih-Teng Wu","doi":"10.1155/stc/6005674","DOIUrl":"https://doi.org/10.1155/stc/6005674","url":null,"abstract":"<div>\u0000 <p>The identification of surface damage and structural components is critical for structural health monitoring (SHM) in order to evaluate building safety. Recently, deep neural networks (DNNs)–based approaches have emerged rapidly. However, the existing approaches often encounter catastrophic forgetting when the trained model is used to learn new classes of interest. Conventionally, joint training of the network on both the previous and new data is employed, which is time-consuming and demanding for computation and memory storage. To address this issue, we propose a new approach that integrates two continual learning (CL) algorithms, i.e., elastic weight consolidation (EWC) and learning without forgetting (LwF), denoted as EWCLwF. We also investigate two scenarios for a comprehensive discussion, incrementally learning the classes with similar versus dissimilar data characteristics. Results have demonstrated that EWCLwF requires significantly less training time and data storage compared to joint training, and the average accuracy is enhanced by 0.7%–4.5% compared against other baseline references in both scenarios. Furthermore, our findings reveal that all CL-based approaches benefit from similar data characteristics, while joint training not only fails to benefit but performs even worse, which indicates a scenario that can emphasize the advantage of our proposed approach. The outcome of this study will enhance the long-term monitoring of progressively increasing learning classes in SHM, leading to more efficient usage and management of computing resources.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6005674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Damping Ratios of Long-Span Bridges Using Adaptive Modal Extended Kalman Filter","authors":"Xiaoxiong Zhang,&nbsp;Rongli Luo,&nbsp;Jia He,&nbsp;Xugang Hua,&nbsp;Lun Yang,&nbsp;Xiaobin Peng,&nbsp;Can Yang,&nbsp;Zhengqing Chen","doi":"10.1155/stc/1493319","DOIUrl":"https://doi.org/10.1155/stc/1493319","url":null,"abstract":"<div>\u0000 <p>Identification of damping ratio is very important for the assessment of service performance of long-span bridges. In this paper, an adaptive EKF in modal domain, named adaptive modal EKF (AMEKF), is proposed for identifying the damping ratios of long-span bridges. The dominant modes are selected, and the dimension of the extended state vector is significantly reduced with the aid of modal coordinate and the corresponding modal transformation. Then, the EKF principle is employed for the identification in modal domain. Moreover, an innovation-based procedure is presented to adaptively adjust the covariance matrix of process noise for the purpose of assuring the parametric identification accuracy. A forgetting factor is employed to put proper weights for the previous and current estimates in each time step. A merit of the proposed approach is that all the damping ratios of the selected modes can be simultaneously identified. The effectiveness of the proposed approach is numerically verified via a long-span suspension bridge. The dynamic tests on a simply supported overhanging steel beam and an aeroelastic model of some long-span suspension bridge are further used for the validation. Results show that the proposed approach is capable of identifying damping ratios with acceptable accuracy.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1493319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Approach for Damping Identification of Stay Cables Under Vortex-Induced Vibrations","authors":"Jiren Zhang,&nbsp;Zhouquan Feng,&nbsp;Jinyuan Dai,&nbsp;Yafei Wang,&nbsp;Xugang Hua,&nbsp;Wang-Ji Yan","doi":"10.1155/stc/5532528","DOIUrl":"https://doi.org/10.1155/stc/5532528","url":null,"abstract":"<div>\u0000 <p>As the span of cable-stayed bridges increases, so does the length of stay cables, making cable vortex-induced vibrations (VIVs) more prominent. This is particularly evident in higher-order multimodal VIVs, which are closely linked to the damping characteristics of the cables. Traditional operational modal analysis (OMA) methods often fail under VIV conditions due to the inadequacy of the white noise excitation assumption. Moreover, potential influences from ambient vibrations and noise contamination introduce further uncertainties into the identification results. This paper addresses these challenges by proposing a novel Bayesian method for damping identification from measured VIV responses. The proposed method, based on a single-degree-of-freedom (SDOF) vortex-induced force model and the statistical properties of the power spectral density of the VIV measurements, aims to enhance the accuracy of damping identification while effectively quantifying uncertainties of identified results. The efficacy of the proposed method is validated through simulated scenarios and applied to the field test of a stay cable in the Sutong Bridge. The results not only demonstrate the method’s high accuracy in identifying damping ratios under VIV but also highlight its capability to effectively quantify the uncertainties in the identification results. This method offers a reliable approach for investigating the evolution of damping in VIV of stay cables and enhances the understanding of the mechanisms behind higher-order multimodal VIV.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5532528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}