Structural Control & Health Monitoring最新文献

Efficient Measurement of Structural Defect Depth Using Parallel Laser Line-Camera System 利用平行激光线相机系统有效测量结构缺陷深度

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-29 DOI: 10.1155/stc/1599724

Chaobin Li, R. K. L. Su

{"title":"Efficient Measurement of Structural Defect Depth Using Parallel Laser Line-Camera System","authors":"Chaobin Li, R. K. L. Su","doi":"10.1155/stc/1599724","DOIUrl":"https://doi.org/10.1155/stc/1599724","url":null,"abstract":"<div>\u0000 <p>The precise depth measurement of common structural defects, such as bulging, delamination, and spalling, is paramount in building condition assessment. This paper presents an efficient and portable parallel laser line-camera system designed for accurately reconstructing defect depth profiles from projected laser stripes. The system features a telescopic design to enhance the measurement range and operational flexibility. Central to its efficacy is a machine learning–aided image processing algorithm that facilitates both robust and highly accurate depth measurements. Specifically, advanced deep learning techniques are applied to detect and segment laser stripes from background interference. A novel hypothesis optimization (HO) algorithm, grounded in a three-layer backpropagation (BP) neural network, is proposed to reduce errors in laser baseline recovery caused by image distortion further. Comprehensive laboratory and field experiments validate the measurement accuracy and superior noise suppression capabilities of the system. Additionally, the paper studies potential errors that could emerge during field operations, thereby confirming the practical utility of the device. The proposed system quickly generates surface profiles in a single shot, making it a valuable tool for monitoring uneven objects.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1599724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884239","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

Numerical and Experimental Analysis of Multifrequency Composite Synchronization of Four Motors in a Vibrating System With the Modified Fuzzy Adaptive Sliding Model Controlling Method 基于改进模糊自适应滑模控制方法的振动系统四电机多频复合同步的数值与实验分析

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-28 DOI: 10.1155/stc/9920013

Lei Jia, Qingsong Chang, Yang Tian, Xin Zhang, Ziliang Liu

{"title":"Numerical and Experimental Analysis of Multifrequency Composite Synchronization of Four Motors in a Vibrating System With the Modified Fuzzy Adaptive Sliding Model Controlling Method","authors":"Lei Jia, Qingsong Chang, Yang Tian, Xin Zhang, Ziliang Liu","doi":"10.1155/stc/9920013","DOIUrl":"https://doi.org/10.1155/stc/9920013","url":null,"abstract":"<div>\u0000 <p>This article addresses the multifrequency composite synchronization of four motors within a vibrating system. Multifrequency synchronization is commonly utilized in engineering due to its effectiveness in screening mixed materials of varying shapes and stickiness. The frequency ratio parameter <i>n</i> influences both the efficiency of the screening process and the overall screening results. Although multifrequency self-synchronization motion can be realized, it can only be realized for integer frequency doubling (<i>n</i> = 2 and <i>n</i> = 3), which limits the diversity of material screening types. By introducing the multifrequency controlled synchronization method, the multifrequency synchronization with noninteger frequencies (<i>n</i> = 1.1–1.9) can be realized, which requires much cost on electrical equipment. To solve this problem, the multifrequency composite synchronization method in this article is proposed. The electromechanical coupling dynamics model of the vibration system is constructed by the Lagrange energy equation. Then, the synchronous condition and stability criteria are derived via the multiscale method by combining the speeds with phase differences. A novel fuzzy adaptive sliding model controlling method associated with a master–slave controlling strategy is introduced to realize multifrequency composite synchronization. The results show that speed errors in different frequencies are only 1000% and 3000%, respectively, and the swing response of the vibration system is small. It presents that the vibration system can not only realize the material screening stably and effectively but also reduce the cost of electrical equipment. The proposed method provides a new reference for multifrequency screening equipment.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9920013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880162","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

Automatic Identification of Diverse Tunnel Threats With Machine Learning–Based Distributed Acoustic Sensing 基于机器学习的分布式声传感隧道威胁自动识别

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-28 DOI: 10.1155/stc/9780866

Taiyin Zhang, Cheng-Cheng Zhang, Tao Xie, Xiaomin Xu, Bin Shi

{"title":"Automatic Identification of Diverse Tunnel Threats With Machine Learning–Based Distributed Acoustic Sensing","authors":"Taiyin Zhang, Cheng-Cheng Zhang, Tao Xie, Xiaomin Xu, Bin Shi","doi":"10.1155/stc/9780866","DOIUrl":"https://doi.org/10.1155/stc/9780866","url":null,"abstract":"<div>\u0000 <p>As the backbone of modern urban underground traffic space, tunnels are increasingly threatened by natural disasters and anthropogenic activities. Current tunnel surveillance systems often rely on labor-intensive surveys or techniques that only target specific tunnel events. Here, we present an automated tunnel monitoring system that integrates distributed acoustic sensing (DAS) technology with ensemble learning. We develop a fiber-optic vibroacoustic dataset of tunnel disturbance events and embed vibroscape data into a common feature space capable of describing diverse tunnel threats. On the scale of seconds, our anomaly detection pipeline and data-driven stacking ensemble learning model enable automatically identifying nine types of anomalous events with high accuracy. The efficacy of this intelligent monitoring system is demonstrated through its application in a real-world tunnel, where it successfully detected a low-energy but dangerous water leakage event. The highly generalizable machine learning model, combined with a universal feature set and advanced sensing technology, offers a promising solution for the autonomous monitoring of tunnels and other underground spaces.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9780866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880161","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

A Bridge Crack Detection and Localization Approach for Unmanned Aerial Systems Using Adapted YOLOX and UWB Sensors 基于YOLOX和UWB传感器的无人机系统桥梁裂缝检测与定位方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-23 DOI: 10.1155/stc/3621939

Mida Cui, Yujie Yan, Dongming Feng, Gang Wu, Zewen Zhu

{"title":"A Bridge Crack Detection and Localization Approach for Unmanned Aerial Systems Using Adapted YOLOX and UWB Sensors","authors":"Mida Cui, Yujie Yan, Dongming Feng, Gang Wu, Zewen Zhu","doi":"10.1155/stc/3621939","DOIUrl":"https://doi.org/10.1155/stc/3621939","url":null,"abstract":"<div>\u0000 <p>The management and maintenance of the aging bridges can benefit from an efficient and automatous bridge inspection process, such as crack detection and localization. This paper presents a robust and efficient approach for unmanned aerial vehicle (UAV)-based crack recognition and localization. An adapted YOLOX model is used in the proposed approach to improve accuracy and efficiency of crack recognition, and hence to enable real-time crack recognition from the captured UAV images at the edge-computing devices. In this way, non-crack images can be recognized in real-time during data acquisition and be filtered out to relieve the burden of subsequent data recording. In addition, a self-organizing positioning system based on ultra-wide-band (UWB) sensors is employed in the proposed system to enable real-time UAV positioning and crack localization in GNSS-denied areas such as spaces underneath the bridge deck. Experiment studies were carried out to investigate the impact of the quantities of employed UWB base stations on the UAV positioning accuracy. Finally, the proposed approach is tested on a self-developed UAV system and the effectiveness is validated through laboratory tests and real-world field tests.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3621939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861781","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

An Advanced Computer Vision Method for Noncontact Vibration Measurement of Cables in Cable-Stayed Bridges 用于斜拉桥电缆非接触振动测量的先进计算机视觉方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-23 DOI: 10.1155/stc/1254049

Naiwei Lu, Weiming Zeng, Jian Cui, Yuan Luo, Xiaofan Liu, Yang Liu

{"title":"An Advanced Computer Vision Method for Noncontact Vibration Measurement of Cables in Cable-Stayed Bridges","authors":"Naiwei Lu, Weiming Zeng, Jian Cui, Yuan Luo, Xiaofan Liu, Yang Liu","doi":"10.1155/stc/1254049","DOIUrl":"https://doi.org/10.1155/stc/1254049","url":null,"abstract":"<div>\u0000 <p>With the development of computer and image processing technologies, computer vision (CV) has been attracting increasing attention in the field of civil engineering measurement and monitoring. Cables in slender structures have unique challenges for CV-based vibration measurement methods, such as low pixel proportion and sensitivity to environmental conditions. This study proposes a noncontact vibration measurement method based on a line tracking algorithm (LTA). The robustness and applicability of the proposed method under varying image resolutions, signal-to-noise ratios, and cable inclination angles were systematically evaluated through experimental test of a cable specimen. To validate the effectiveness of the proposed method for practical detection applications, a vibration test on a scaled cable-stayed bridge model was carried out. The numerical result indicates that the LTA provides high reliability and accuracy values of the cable force. The maximum errors of the first-order self-vibration frequency and cable force of the scaled cable-stayed bridge is 0.99% and 2%, respectively. The proposed method maintains strong stability across various conditions, which provides a reference for long-term structural health monitoring of cable-stayed bridges.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1254049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866016","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

Fatigue Monitoring of 321 Steel Coated by Laser Additively Manufactured CoCrFeMnNi High-Entropy Alloy Using Acoustic Emission Technique 激光增材制备CoCrFeMnNi高熵合金涂层321钢的声发射疲劳监测

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-22 DOI: 10.1155/stc/9115819

Wei Li, Shengnan Hu, Shunpeng Zhu, Cong Li, Guowei Bo, Chipeng Zhang, Dapeng Jiang, Hui Chen, Jianjun He, Wenjun Duan, Jian Chen

{"title":"Fatigue Monitoring of 321 Steel Coated by Laser Additively Manufactured CoCrFeMnNi High-Entropy Alloy Using Acoustic Emission Technique","authors":"Wei Li, Shengnan Hu, Shunpeng Zhu, Cong Li, Guowei Bo, Chipeng Zhang, Dapeng Jiang, Hui Chen, Jianjun He, Wenjun Duan, Jian Chen","doi":"10.1155/stc/9115819","DOIUrl":"https://doi.org/10.1155/stc/9115819","url":null,"abstract":"<div>\u0000 <p>Fatigue failure is a common mode of deterioration for steel cables (e.g., 321 stainless steel) in cable-stayed bridges. In this case, given that the FeCoNiCrMn high-entropy alloy (HEA) coatings have been found to simultaneously improve the fatigue and corrosion resistance of 321 steel, the fatigue crack growth behavior of 321 steel coated with selective laser melting CoCrFeMnNi HEA was further studied in this work. The results indicate that the CoCrFeMnNi alloy coating is able to increase the fatigue crack growth resistance of 321 steel by 21.43% compared to the uncoated 321 steel, and this is because the initiation of crack is mitigated by the angular disparities between adjacent grains and an increased dislocation density in the coating. Furthermore, the acoustic emission (AE) technique was used to track fatigue damage and predict fatigue crack growth life. It was found that crack length could be effectively monitored and predicted using the count and energy parameter, suggesting material and stress ratio independence in the AE technique.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9115819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856746","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

Multiscenario Generalization Crack Detection Network Based on the Visual Foundation Model 基于可视化基础模型的多场景泛化裂纹检测网络

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-21 DOI: 10.1155/stc/6269747

Shiwei Luo, Xiongyao Xie, Biao Zhou, Kun Zeng, Jun Guo

{"title":"Multiscenario Generalization Crack Detection Network Based on the Visual Foundation Model","authors":"Shiwei Luo, Xiongyao Xie, Biao Zhou, Kun Zeng, Jun Guo","doi":"10.1155/stc/6269747","DOIUrl":"https://doi.org/10.1155/stc/6269747","url":null,"abstract":"<div>\u0000 <p>Recently, convolutional neural networks (CNNs) and hybrid networks, which integrate CNN with Transformer, have been widely employed in structuring crack detection, effectively addressing the challenges of high-precision crack identification in controlled scenes. However, scene generalization remains a significant challenge for existing networks, especially under limited dataset conditions. With the rapid development of foundation models (like ChatGPT), achieving scene generalization has become feasible. In this paper, by taking tunnel crack detection as the background, the CraSAM network is proposed, which incorporates a foundation model-based encoder and a prompt transfer learning module. Based on six datasets including tunnel, bridge, building, and pavement, the CraSAM is compared with 15 state-of-the-art models, including Unet, DeepLabv3+, SSSeg, and TransUNet. It exhibits superior generalization capability both on few-sample learned and unlearned conditions. This work will benefit to investigate of new ways for the utilization of the visual foundation model in various professional fields.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6269747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856799","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

One-Dimensional Deep Convolutional Neural Network-Based Intelligent Fault Diagnosis Method for Bearings Under Unbalanced Health and High-Class Health States 基于一维深度卷积神经网络的轴承不平衡健康和高健康状态智能故障诊断方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-17 DOI: 10.1155/stc/6498371

Temesgen Tadesse Feisa, Hailu Shimels Gebremedhen, Fasikaw Kibrete, Dereje Engida Woldemichael

{"title":"One-Dimensional Deep Convolutional Neural Network-Based Intelligent Fault Diagnosis Method for Bearings Under Unbalanced Health and High-Class Health States","authors":"Temesgen Tadesse Feisa, Hailu Shimels Gebremedhen, Fasikaw Kibrete, Dereje Engida Woldemichael","doi":"10.1155/stc/6498371","DOIUrl":"https://doi.org/10.1155/stc/6498371","url":null,"abstract":"<div>\u0000 <p>Modern industrial systems depend heavily on rotating machines, especially rolling element bearings (REBs), to facilitate operations. These components are prone to failure under harsh and variable operating conditions, leading to downtime and financial losses, which emphasizes the need for accurate REB fault diagnosis. Recently, interest has surged in using deep learning, particularly convolutional neural networks (CNNs), for bearing fault diagnosis. However, training CNN models requires extensive data and balanced bearing health states, which existing methods often assume. In addition, while practical scenarios encompass a diverse range of bearing fault conditions, current methods often focus on a limited range of scenarios. Hence, this paper proposes an enhanced method utilizing a one-dimensional deep CNN to ensure reliable operation, with its effectiveness evaluated on Case Western Reserve University (CWRU) rolling bearing datasets. The experimental results showed that the diagnostic accuracy reached 100% under 0∼3 hp working loads for 10 unbalanced health classes. Moreover, it attained 100% accuracy for high-class health states with 20, 30, and 40 classes, and when extended to 64 health classes, it reached a peak accuracy of 99.96%. Thus, the method achieved improved classification ability and stability by employing a straightforward model architecture, along with the integration of batch normalization and dropout operations. Comparative analysis with existing diagnostic methods further underscores the model superiority, particularly in scenarios involving unbalanced and high-class health states, thus emphasizing its effectiveness and robustness. These findings significantly advance the field of intelligent bearing fault diagnosis.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6498371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846001","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

Hybrid-Driven Digital Twin Framework for Time-Variant Reliability Assessment of Civil Structures 土木结构时变可靠性评估的混合驱动数字孪生框架

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-16 DOI: 10.1155/stc/1167999

Yu Xin, Yu-Sen Cai, Zuo-Cai Wang, Jun Li, Wei-Chao Hou, Chao Li

{"title":"Hybrid-Driven Digital Twin Framework for Time-Variant Reliability Assessment of Civil Structures","authors":"Yu Xin, Yu-Sen Cai, Zuo-Cai Wang, Jun Li, Wei-Chao Hou, Chao Li","doi":"10.1155/stc/1167999","DOIUrl":"https://doi.org/10.1155/stc/1167999","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel hybrid-driven digital twin (DT) framework for time-variant reliability assessment of civil structures, which mainly consists of four modules, including physics model construction, data-driven model calibration, failure probability calculation, and time-variant reliability prediction. In the first module, a DT model of a specific structure is constructed to simulate structural dynamic responses. Then, an improved unscented Kalman filter (IUKF) algorithm is performed to continuously calibrate the parameters of DT model. Subsequently, in module 3, the subset simulation (SS) approach is employed to calculate failure probability of structures subjected to various model parameter samples, and the generated input–output samples are further applied for metamodel training. A Kriging metamodeling is used to construct the correlation between model parameters and structural failure probability. Once the metamodel is well trained, the time-variant reliability assessment of structures can be continuously achieved in module 4. Numerical simulations on a Bouc–Wen model are conducted to validate the feasibility and accuracy of the proposed approach. Furthermore, a scaled column shake table structure is further employed to verify the effectiveness of the proposed approach. Both numerical and experimental results have shown that the proposed approach is capable of conducting time-variant reliability assessment of civil structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1167999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836041","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

An Unsupervised Structural Damage Diagnosis Method Based on Deep Learning and Sensor Interrelationships 基于深度学习和传感器相互关系的无监督结构损伤诊断方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-04-15 DOI: 10.1155/stc/8821227

Wen-Sheng Zhang, Hong-Nan Li, Xing Fu, Zheng-Li Gu

{"title":"An Unsupervised Structural Damage Diagnosis Method Based on Deep Learning and Sensor Interrelationships","authors":"Wen-Sheng Zhang, Hong-Nan Li, Xing Fu, Zheng-Li Gu","doi":"10.1155/stc/8821227","DOIUrl":"https://doi.org/10.1155/stc/8821227","url":null,"abstract":"<div>\u0000 <p>This paper presents a novel unsupervised method for structural damage diagnosis, which transforms the problem of structural damage diagnosis into the problem of identifying anomalous data in monitoring data. The method establishes the sensor interrelationships based on the graph structure, optimizes the hyperparameters of the graph neural network (GNN) model, and realizes the structural response prediction. By calculating the discrepancy between the predicted response and the monitoring data, the method identifies the anomalies to facilitate the identification and localization of structural damage. The efficiency of the proposed method for bolt loosening detection was evaluated through the analysis of acceleration data collected from a vibrating grandstand simulator and strain data from a wind tunnel test of a scaled tower model. The experimental results indicated that the established connections can provide a preliminary indication of the relative importance of the sensors, which may also be regarded as a metric for each node in the structure. The proposed method is effective in the detection and localization of minor damage in infrastructure structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8821227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831401","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