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

Structural Dynamic Response Reconstruction Based on Recurrent Neural Network–Aided Kalman Filter 基于递归神经网络辅助卡尔曼滤波器的结构动态响应重构

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-23 DOI: 10.1155/2024/7481513

Yiqing Wang, Mingming Song, Ao Wang, Limin Sun

{"title":"Structural Dynamic Response Reconstruction Based on Recurrent Neural Network–Aided Kalman Filter","authors":"Yiqing Wang, Mingming Song, Ao Wang, Limin Sun","doi":"10.1155/2024/7481513","DOIUrl":"https://doi.org/10.1155/2024/7481513","url":null,"abstract":"<div>\u0000 <p>In structural health monitoring (SHM), an important issue is the limited availability of measurement data due to the spatial sparsity of sensors installed on the structure. These measurements are insufficient to accurately depict the actual dynamic behavior and response of the structure. Therefore, full-field (i.e., every degree of freedom) structural response reconstruction based on sparse measured data has drawn a lot of attention in recent years. Kalman filter (KF) is an effective technology for response reconstruction (also known as state estimation), providing an optimal solution for systems that can be well-represented by a fully known Gaussian linear state-space model. This implies that both the process noise and measurement noise follow known zero-mean Gaussian distribution, which is impractical in many civil engineering applications considering the unavoidable modeling errors and variations of environmental conditions. To address this challenge, a data-physics hybrid-driven method, i.e., KalmanNet, is proposed in this study for response reconstruction of partially known systems. By integrating a recurrent neural network (RNN) module into the KF framework, KalmanNet can efficiently learn and compute the Kalman gain using available monitoring data, without any Gaussian assumptions or explicit noise covariance specifications (e.g., covariance matrices of process and measurement noise). Both numerical and experimental investigations are conducted to validate this method. The results demonstrate that under the influence of non-Gaussian noise and modeling errors, KalmanNet can effectively and accurately reconstruct the structural response from sparse measurements in real-time and has higher accuracy and robustness compared to traditional KF even with optimal parameter settings.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7481513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525166","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 Process Criterion for the Concrete Dam in Geomechanical Model Test 地质力学模型试验中混凝土大坝的破坏过程标准

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-23 DOI: 10.1155/2024/4058789

Jianghan Xue, Xiang Lu, Zelin Ding, Chen Chen, Yuan Chen, Jiankang Chen

{"title":"Damage Process Criterion for the Concrete Dam in Geomechanical Model Test","authors":"Jianghan Xue, Xiang Lu, Zelin Ding, Chen Chen, Yuan Chen, Jiankang Chen","doi":"10.1155/2024/4058789","DOIUrl":"https://doi.org/10.1155/2024/4058789","url":null,"abstract":"<div>\u0000 <p>The geomechanical model test (GMT), a means of intuitively exploring the model’s failure modes and revealing failure mechanisms, is considered an effective approach for studying the structural characteristics of dams under complex geological conditions. However, during the overloading process of the model, the catastrophe trends of monitoring data are unclear, and catastrophe points differ at different monitoring sites. These factors have led to large errors in the judgment of researchers regarding the model’s state and misperception of the structural properties during the damage process. In this study, a comprehensive evaluation method for the model’s state intervals in the damage process is proposed. The criterion employed an interval analysis hierarchy process that considered the differences, consistency, and credibility (CDC-IAHP) among multiple decision-makers (DMs), effectively reducing the subjectivity of their judgments. Additionally, this process was combined with cusp catastrophe theory (CCT) to determine whether the model underwent an abrupt change at various overload factors comprehensively. This is the first time that CDC-IAHP and CCT have been combined as criterion for a comprehensive method on the damage process of concrete dams in GMTs, and was applied to the Wudu gravity dam, indicating its applicability is very good. Compared to the researcher’s judgment, this approach is used to analyze and judge the structural state more accurately and scientifically while reducing subjectivity, which can help to better understand the structural characteristics and bearing capacity of actual engineering projects.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4058789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525204","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 Classification in Offshore Structures Under Environmental Variations and Measured Noises Using Linear Discrimination Analysis 利用线性判别分析对环境变化和测量噪声下的近海结构进行结构损伤分类

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-21 DOI: 10.1155/2024/6650582

Yufeng Jiang, Yu Liu, Shuqing Wang

{"title":"Structural Damage Classification in Offshore Structures Under Environmental Variations and Measured Noises Using Linear Discrimination Analysis","authors":"Yufeng Jiang, Yu Liu, Shuqing Wang","doi":"10.1155/2024/6650582","DOIUrl":"https://doi.org/10.1155/2024/6650582","url":null,"abstract":"<div>\u0000 <p>Changing environmental conditions and measured noises often affect the dynamic responses of structures and can obscure subtle changes in the vibration characteristics caused by damage. To address this issue, a new method for classifying damage in offshore structures under varying environmental conditions and measured noises is proposed using linear discrimination analysis (LDA). Two sets of data on dynamic characteristics, one from healthy structures and the other from unknown testing structures, are used to determine the optimal projection vector. This vector is perpendicular to the discriminant hyperplane and is used for damage classification. The damage-sensitive features are extracted by projecting both sets of data onto this vector. These features are then used with the hypothesis test technique to determine the condition state of the testing structure. Numerical studies on offshore wind turbine structures and experimental validations of a deep-sea mining system are being conducted to evaluate the effectiveness of the proposed approach. The study also examines the impact of mode combinations, measured noises and samples on the performance of the approach. The results indicate that the proposed approach can accurately assess the structural health state even in the presence of environmental changes and noise contamination, even with limited samples. The promising performance of the approach will facilitate the establishment of an online structural monitoring system to ensure the safety of offshore structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6650582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524841","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

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