Structural Control & Health Monitoring最新文献_第8页

Intelligent Tension Correction Method for EME Sensors considering Torsion Effect of Wire Rope Suspender Cables 考虑钢丝绳悬挂电缆扭转效应的 EME 传感器智能张力修正方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-18 DOI: 10.1155/2024/3417038

Yuanfeng Duan, Wei Wei, Ru Zhang, J. J. Roger Cheng

{"title":"Intelligent Tension Correction Method for EME Sensors considering Torsion Effect of Wire Rope Suspender Cables","authors":"Yuanfeng Duan, Wei Wei, Ru Zhang, J. J. Roger Cheng","doi":"10.1155/2024/3417038","DOIUrl":"https://doi.org/10.1155/2024/3417038","url":null,"abstract":"<div>\u0000 <p>Long-term and accurate monitoring of suspender cable tensions is particularly important for safe evaluation of cable suspension bridges or tied-arch bridges. Torsional deformation, commonly present in wire rope suspender cables (WR cables) during tensioning construction or in-service, has not been considered in the elasto-magneto-electric (EME) sensor system. This study investigated the effects of torsion on tension measurement and proposed an intelligent correction method without measuring the torsion angles per unit length. A calibration platform for full-scale WR cable is established with a rotation angle fixing device. Tension calibration experiments were carried out under free rotation condition without activating the angle fixing device and under various fixed rotation conditions by setting a series of initial fixed angles at the anchor head. It was found that the relative error for the EME sensor using the traditional calibration method under the free rotation condition could reach 11.72%. To improve the accuracy, an intelligent tension correction method for the torsion effect is proposed, which uses the experimental signals in various fixed conditions and the backpropagation neural network with K-fold cross-validation. The parameters of the BPNN were optimized by genetic algorithm, and it was found that the maximum relative error decreases from 11.72% to 5.24% and the maximum absolute error decreases from 21.75 kN to 14.67 kN for the condition of free rotation. Finally, the EME sensor with intelligent tension correction method was applied to a real suspension bridge. The measurement relative error of the field test decreases from 6.60% without the torsion compensation to 2.80% with the torsion compensation, which indicate that the proposed intelligent tension correction method can ensure the accurate tension measurement of the WR cables by the EME sensor.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3417038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448996","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}

引用次数: 0

Structural Damage Diagnosis of Aerospace CFRP Components: Leveraging Transfer Learning in the Matching Networks Framework 航空 CFRP 组件的结构损伤诊断：利用匹配网络框架中的迁移学习

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-16 DOI: 10.1155/2024/2341211

Zhuojun Xu, Hao Li, Jianbo Yu

{"title":"Structural Damage Diagnosis of Aerospace CFRP Components: Leveraging Transfer Learning in the Matching Networks Framework","authors":"Zhuojun Xu, Hao Li, Jianbo Yu","doi":"10.1155/2024/2341211","DOIUrl":"https://doi.org/10.1155/2024/2341211","url":null,"abstract":"<div>\u0000 <p>This paper introduces a damage diagnosis method based on the reassignment method and matching networks (MNs) to study the structural health monitoring of aerospace composite material components. This aims to facilitate the mapping of signal features to complex failure modes. We introduce a signal processing technique based on the reassignment method, employing a sliding analysis window to re-estimate local instantaneous frequency and group delay. By utilizing the short-time phase spectrum of the signal, we correct the nominal time and frequency coordinates of the spectrum data, aligning them more accurately with the true support region of the analyzed signal. Subsequently, this paper developed a deep matching network (DMN) damage diagnosis model based on MNs. This model utilizes a convolutional neural network (CNN) to extract damage-related features from the signal and introduces the full context embedding (FCE) method to enhance the compatibility of sample embeddings. In this process, the embeddings of each sample in the training set should be mutually independent, while the embeddings of test samples should be regulated by the distribution of training set sample data. Ultimately, the damage category of test samples is determined based on cosine similarity. We validate our model using damage sample data collected from experiments and simulations conducted under varying components and operating conditions. Comparative assessments with five mainstream methods reveal an average accuracy exceeding 96%. This underscores the exceptional recognition accuracy and generalization performance of our proposed method in cross-operating condition fault diagnosis experiments concerning aircraft composite material components.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2341211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443567","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}

引用次数: 0

Flutter Suppression Effects of Movable Vertical Stabilizers on Suspension Bridges With Steel Box Girders 钢箱梁悬索桥上可移动垂直稳定器的扑翼抑制效果

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-16 DOI: 10.1155/2024/8729243

Rui Zhou, Dong Xiao, Genshen Fang, Yongxin Yang, Yaojun Ge, Haojun Xu, Yufei Wu

{"title":"Flutter Suppression Effects of Movable Vertical Stabilizers on Suspension Bridges With Steel Box Girders","authors":"Rui Zhou, Dong Xiao, Genshen Fang, Yongxin Yang, Yaojun Ge, Haojun Xu, Yufei Wu","doi":"10.1155/2024/8729243","DOIUrl":"https://doi.org/10.1155/2024/8729243","url":null,"abstract":"<div>\u0000 <p>As the combination of springs and vertical stabilizers, the movable downward vertical central stabilizer (MDVCS) is proposed to further control the nonlinear flutter of super long-span suspension bridges in this paper. A series of flutter suppression tests of closed-box girders and twin-box steel girders with various MDVCSs are conducted. Based on the coupled flutter theoretical method, the sensitivity analysis of two important parameters including the height and stiffness of MDVCS are carried out to compare their nonlinear flutter control mechanism. The results show that the flutter critical wind speed (<i>U</i><sub>cr</sub>) of the closed-box girder continued to increase with the decrease of the height of the DVCS and the increase of spring stiffness, whereas the <i>U</i><sub>cr</sub> of the twin-box girder increased at first and then decreased. The cubic polynomial function and quadratic Holliday function are suitable to modify the correction coefficients of <i>U</i><sub>cr</sub> for the closed-box girder with various stiffnesses and heights of MDVCS, while the Lorentz peak-value function and cubic polynomial function are suitable to modify the <i>U</i><sub>cr</sub> of the twin-box girder. Furthermore, the MDVCS significantly changes the rules of two positive and negative aerodynamic damping ratios for the closed-box girder and two negative aerodynamic damping ratios for the twin-box girder. Besides, the peak vertical displacement amplitudes of the box girder are about half of the MDVCS, since both the height and stiffness of MDVCS alter the elliptical radius of vertical phase planes to affect the limit cycle oscillation of soft flutter, especially for the leeward MDVCS for the twin-box girder.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8729243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443520","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}

引用次数: 0

Reliable Model Predictive Vibration Control for Structures with Nonprobabilistic Uncertainties 针对具有非概率不确定性的结构的可靠模型预测振动控制

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-15 DOI: 10.1155/2024/7596923

Jinglei Gong, Xiaojun Wang

{"title":"Reliable Model Predictive Vibration Control for Structures with Nonprobabilistic Uncertainties","authors":"Jinglei Gong, Xiaojun Wang","doi":"10.1155/2024/7596923","DOIUrl":"https://doi.org/10.1155/2024/7596923","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel reliable model predictive control (MPC) method for active vibration control of structure with nonprobabilistic uncertainties. First, the framework of reliable MPC is established by integrating nonprobabilistic reliability constraints into nominal MPC. Based on the first-order Taylor expansion and first-passage theory, an efficient nonprobabilistic reliability analysis method that is suitable for online computation is proposed. A nonprobabilistic Kalman filter is further proposed for determine system states and their uncertain region. Unlike most robust MPC approaches, the proposed reliable MPC focuses on the satisfaction of state constraints in terms of structural reliability and is more suitable for structures with stringent safety requirements. Compared to existing reliability-based vibration control methods, reliable MPC requires no knowledge of disturbance and exhibits greater adaptability to load environments. The effectiveness and superiority of the proposed reliable MPC are validated through a numerical example and an engineering case study.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7596923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435442","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}

引用次数: 0

Residual Convolutional Attention Model With Transfer Learning for Detecting Multianomalous Features in Structural Vibration Data 利用迁移学习的残差卷积注意力模型检测结构振动数据中的多异常特征

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-14 DOI: 10.1155/2024/2451763

Tao Li, Zhongyu Zhang, Rui Hou, Kangkang Zheng, Dongwei Ren, Ruiqi Yuan, Xinyu Jia

{"title":"Residual Convolutional Attention Model With Transfer Learning for Detecting Multianomalous Features in Structural Vibration Data","authors":"Tao Li, Zhongyu Zhang, Rui Hou, Kangkang Zheng, Dongwei Ren, Ruiqi Yuan, Xinyu Jia","doi":"10.1155/2024/2451763","DOIUrl":"https://doi.org/10.1155/2024/2451763","url":null,"abstract":"<div>\u0000 <p>In response to the data anomalies and frequent false alarms caused by harsh environments in long-term structural health monitoring (SHM), this study has reframed the detection of abnormal vibration data as a time series classification problem. This approach identifies multiple anomalous features, thereby reducing manual detection costs. The novel developed Convolutional Neural Network with Squeeze-and-Excitation and Multi-Head Self-Attention (CNN–SE–MHSA) employs a deep residual network structure with channel and spatial attention mechanisms, effectively handling the global long-term dependencies required for anomaly feature learning. It better understands and utilizes feature information across different levels and dimensions, enhancing classification accuracy in complex anomaly situations. Through t-SNE dimensionality reduction visualization and interpretability analysis, it is demonstrated that the model excels in identifying critical features. Furthermore, by generating simulated data with a variational autoencoder (VAE) and implementing transfer learning strategies based on these data, the issue of low recognition accuracy for complex anomaly data due to data imbalance can be effectively mitigated. In a 25-day long-term monitoring experiment of indoor tunnel lining structures, this method demonstrated an average accuracy rate exceeding 96% and a rapid detection capability within 16 min. The results indicate that this method achieves high accuracy in anomaly detection for long-term monitoring data, even when relying exclusively on time-domain data.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2451763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435529","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}

引用次数: 0

Displacement Measurement and 3D Reconstruction of Segmental Retaining Wall Using Deep Convolutional Neural Networks and Binocular Stereovision 利用深度卷积神经网络和双目立体视觉进行分段式挡土墙的位移测量和三维重建

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-14 DOI: 10.1155/2024/9912238

Minh-Vuong Pham, Yun-Tae Kim, Yong-Soo Ha

{"title":"Displacement Measurement and 3D Reconstruction of Segmental Retaining Wall Using Deep Convolutional Neural Networks and Binocular Stereovision","authors":"Minh-Vuong Pham, Yun-Tae Kim, Yong-Soo Ha","doi":"10.1155/2024/9912238","DOIUrl":"https://doi.org/10.1155/2024/9912238","url":null,"abstract":"<div>\u0000 <p>Computer vision techniques were employed to monitor the displacement of retaining walls using artificial markers, traditional feature detection algorithms, and photogrammetry-based point cloud reconstruction. However, the use of artificial markers often increases both installation time and costs, whereas the performance of traditional feature matching is affected by uneven illumination, and photogrammetry techniques require multiple images for point cloud reconstruction. To overcome these limitations, a nontarget-based displacement monitoring method for segmental retaining walls (SRWs) using a combination of deep learning and stereovision was proposed. Binocular stereovision was employed to reconstruct the geometry and surface properties of the SRW in a digital three-dimensional (3D) model. Deep learning models were then used to extract natural features from SRW blocks, enabling displacement calculation without using artificial targets. The performance was evaluated by monitoring the behaviors of SRW experiments at both laboratory and field scales. The deep learning–based image segmentation models identified SRW block features in the experiment and real case datasets with an average F1 score from 0.910 to 0.965 under various environmental conditions. The reconstructed results of point cloud coordinates demonstrated high accuracy, ranging from 95.2% to 98.6%. Furthermore, the calculated displacement exhibited a high degree of agreement with the measured displacement. The accuracy of the calculated displacements for the laboratory and field experiments ranged from 89.5% to 99.1%. The proposed method can be used for automatic SRW displacement monitoring.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/9912238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435466","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}

引用次数: 0

Performance and Characteristics of Sprayed Flexible Sensor for Strain Monitoring of Steel Bridges 用于钢桥应变监测的喷涂柔性传感器的性能和特点

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-14 DOI: 10.1155/2024/2966457

Qing-Hua Zhang, Jun Chen, Qi-Bin Huang, Shao-Bing Shao, Chuang Cui

{"title":"Performance and Characteristics of Sprayed Flexible Sensor for Strain Monitoring of Steel Bridges","authors":"Qing-Hua Zhang, Jun Chen, Qi-Bin Huang, Shao-Bing Shao, Chuang Cui","doi":"10.1155/2024/2966457","DOIUrl":"https://doi.org/10.1155/2024/2966457","url":null,"abstract":"<div>\u0000 <p>Monitoring stress and strain at the critical details of steel bridges is essential for ensuring structural integrity. This study introduces a three-layer flexible strain sensor produced through a spraying process, using flake-shaped silver-coated copper powder as the conductive filler and modified acrylic emulsion as the matrix material. The study investigated the impact of size parameters on sensor sensitivity, determining optimal dimensions of 20 mm in length, 2 mm in width, and an initial resistance value ranging from 1.0 Ω to 1.8 Ω. Analysis of the optimized sensor’s performance unveiled high sensitivity and linear response capabilities under low strain conditions with a gauge factor (GF) value of up to 25.6 and a linear correlation coefficient <i>R</i><sup>2</sup> ≥ 0.971 under 300 με. Notably, the sensor exhibits an extremely low strain detection limit of 0.005% and a broad response range spanning from 0.005% to 0.19% strain. It demonstrates swift response and recovery times of 500–800 ms, showcases directional strain response, exhibits good repeatability, and endures durability tests (withstanding 3000 cycles). Furthermore, a fitting formula is proposed to accurately depict the strain and relative resistance change relationship across a wide response range. The study and initial application of this sensor’s sensing characteristics and performance signify its potential for practical engineering applications.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2966457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435527","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}

引用次数: 0

Dynamic Behaviors of a Two-Cable Network With Two Negative Stiffness Dampers and a Cross-Tie 带有两个负刚度阻尼器和一个交叉拉杆的双缆网络的动态行为

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-10 DOI: 10.1155/2024/4254998

Mengyu Li, Yanwei Xu, Hui Gao, Zhipeng Cheng, Zhihao Wang

{"title":"Dynamic Behaviors of a Two-Cable Network With Two Negative Stiffness Dampers and a Cross-Tie","authors":"Mengyu Li, Yanwei Xu, Hui Gao, Zhipeng Cheng, Zhihao Wang","doi":"10.1155/2024/4254998","DOIUrl":"https://doi.org/10.1155/2024/4254998","url":null,"abstract":"<div>\u0000 <p>Due to their structural characteristics, stay cables are inherently susceptible to vibrations. Addressing this issue, the research explores the dynamics of a two-cable network system, emphasizing the impact of composite vibration control methods. A system consisting of two horizontal cables is presented, each fitted with negative stiffness dampers (NSDs) at their anchored ends and interconnected by a cross-tie. A complex eigenvalue equation, formulated based on displacement boundary conditions and the continuity of displacement and force, is validated through numerical simulations. The multimode damping effects of the dual NSDs and cross-tie on the two-cable network are explored through parameter analysis and optimization. The results demonstrate that reducing the stiffness of the cross-tie improves the fundamental modal damping ratio, whereas increasing its stiffness or positioning it close to the cable’s midpoint enhances the vibration frequency. The incorporation of NSDs into the hybrid system significantly increases the maximum damping ratio while lowering the optimal damping coefficient. This study presents a method for calculating the range of negative stiffness values, providing insights into the selection of installation positions and stiffness for the cross-tie, thereby facilitating the design of highly effective multimode vibration control solutions for stay cables.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4254998","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429725","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}

引用次数: 0

Damage Identification Using Nonlinear Manifold Learning Method under Changing Environments 在不断变化的环境中使用非线性表单学习法进行损伤识别

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-09-28 DOI: 10.1155/2024/2359214

Peng Guo, Dong-sheng Li, Jie-zhong Huang, Hou Qiao, Hong-nan Li

{"title":"Damage Identification Using Nonlinear Manifold Learning Method under Changing Environments","authors":"Peng Guo, Dong-sheng Li, Jie-zhong Huang, Hou Qiao, Hong-nan Li","doi":"10.1155/2024/2359214","DOIUrl":"https://doi.org/10.1155/2024/2359214","url":null,"abstract":"<div>\u0000 <p>Damage identification is a key aspect of structural health monitoring (SHM). However, any measurement of the structural response can be impacted by environmental and operational variations (EOVs), which can affect the system and hinder damage detection. It is therefore important to distinguish between damage-induced changes in structural dynamic properties and changes caused by EOVs. To address this issue, this paper proposes a damage identification method based on nonlinear manifold learning, specifically Laplacian eigenmaps (LEs). The method eliminates the impact of EOVs on the damage index by treating them as embedded variables and does not require the direct measurement of environmental parameters. The Gaussian process regression (GPR) prediction model results in small residuals when the structure is healthy and significant increases when the structure is damaged, demonstrating the effectiveness of the method in removing environmental influences. The proposed method is demonstrated using computer-simulated data, where the environmental conditions have a nonlinear effect on the vibration features. The proposed LE-GPR algorithm is then applied to the Z24 and KW51 bridges and successfully identifies structural damage. The advantage of the proposed approach is its ability to eliminate the effects of ambient temperature and accurately identify structural damage.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2359214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359821","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}

引用次数: 0

Modal Property-Based Data Anomaly Detection Method for Autonomous Stay-Cable Monitoring System in Cable-Stayed Bridges 基于模态属性的数据异常检测方法用于斜拉桥中的自主留缆监测系统

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-09-27 DOI: 10.1155/2024/8565150

Seunghoo Jeong, Seung-Seop Jin, Sung-Han Sim

引用次数: 0