Structural Control & Health Monitoring最新文献_第5页

Tree-Based Pipeline Optimization-Based Automated-Machine Learning Model for Performance Prediction of Materials and Structures: Case Studies and UI Design 基于树状管道优化的自动机器学习模型，用于材料和结构的性能预测：案例研究与用户界面设计

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-11-07 DOI: 10.1155/2024/1485739

Shixue Liang, Zhengyu Fei, Junning Wu, Xing Lin

{"title":"Tree-Based Pipeline Optimization-Based Automated-Machine Learning Model for Performance Prediction of Materials and Structures: Case Studies and UI Design","authors":"Shixue Liang, Zhengyu Fei, Junning Wu, Xing Lin","doi":"10.1155/2024/1485739","DOIUrl":"https://doi.org/10.1155/2024/1485739","url":null,"abstract":"<div>\u0000 <p>Machine learning (ML) methods have become increasingly prominent for predicting material and structural performance in civil engineering. However, these methods often require repetitive iterations and optimizations by professionals to obtain an optimal model, which are time-consuming and challenging for nonexpert users. In this paper, we propose an automated ML (Auto-ML) model using the tree-based pipeline optimization tool (TPOT) to address these limitations and streamline the performance prediction process. TPOT leverages genetic programming to optimize various ML models, including DT, RF, GBDT, LightGBM, and XGBoost, and to search possible models that fits a particular dataset, which cuts the most tedious parts of ML. To demonstrate the effectiveness of TPOT-based Auto-ML, two case studies are presented by using TPOT-based Auto-ML algorithms to construct prediction models for compressive strength of recycled micropowder mortar, and punching shear bearing capacity/failure mode of RC slab-column joints. To explain the “black box” of Auto-ML, Shapley Additive Explanation (SHAP) is introduced to interpret the best predictive models and rank the importance of influencing factors, providing a basis for material and structural design. Finally, a user interface (UI) for engineering applications is developed which enables end-to-end automation from data preprocessing to predictive results presentation.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1485739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641152","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

Vision Transformer–Based Anomaly Detection Method for Offshore Platform Monitoring Data 基于视觉变压器的海上平台监测数据异常检测方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-11-06 DOI: 10.1155/2024/1887212

Quanhua Zhu, Qingpeng Wu, Yalin Yue, Yuequan Bao, Tao Zhang, Xueliang Wang, Zhentao Jiang, Haozheng Chen

{"title":"Vision Transformer–Based Anomaly Detection Method for Offshore Platform Monitoring Data","authors":"Quanhua Zhu, Qingpeng Wu, Yalin Yue, Yuequan Bao, Tao Zhang, Xueliang Wang, Zhentao Jiang, Haozheng Chen","doi":"10.1155/2024/1887212","DOIUrl":"https://doi.org/10.1155/2024/1887212","url":null,"abstract":"<div>\u0000 <p>The structural health monitoring system for offshore platforms exhibits anomalies in the collected monitoring data due to its prolonged service in complex and harsh environments. These anomalies significantly impede data analysis and early warning capabilities. In order to realize efficient and intelligent anomaly detection for the monitoring data, a method based on the vision transformer (ViT) model is proposed. Firstly, the monitoring data are transformed into image files by segmentation and visualization. Subsequently, the image features are analyzed to identify the anomaly patterns and construct an image database, so that the data anomaly detection problem is transformed into a classification problem based on the image features. Lastly, the ViT model combined with convolutional neural network (CNN) is constructed. The local perception ability of CNN is utilized to extract the underlying image features and smooth the image features inputted into the ViT model, which improves the accuracy of the model. Validation using actual monitoring data shows that the proposed method can efficiently detect multiple types of anomaly patterns in the monitoring data with an accuracy rate of 93.1%.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1887212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596297","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

Investigation of the Mechanism of Hidden Defects in Epoxy Asphalt Pavement on Steel Bridge Decks Under Moisture Diffusion Using Nondestructive Detection Techniques 利用无损检测技术研究钢桥面环氧沥青铺装在湿气扩散条件下隐藏缺陷的机理

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-30 DOI: 10.1155/2024/6490775

Wen Nie, Duanyi Wang, Junjian Yan, Xiaoning Zhang

{"title":"Investigation of the Mechanism of Hidden Defects in Epoxy Asphalt Pavement on Steel Bridge Decks Under Moisture Diffusion Using Nondestructive Detection Techniques","authors":"Wen Nie, Duanyi Wang, Junjian Yan, Xiaoning Zhang","doi":"10.1155/2024/6490775","DOIUrl":"https://doi.org/10.1155/2024/6490775","url":null,"abstract":"<div>\u0000 <p>This study conducts a rigorous analysis of the moisture diffusion mechanism that undermines the adhesive layer of epoxy asphalt (EA) pavement on steel bridge decks, thereby fostering latent distresses. Furthermore, a novel and highly efficacious approach for detecting these concealed distresses is introduced. The findings of water vapor permeability tests conclusively reveal that the moisture diffusion coefficients of the upper and lower layers of the EA pavement stand at 0.1238 mm<sup>2</sup>/s and 0.0879 mm<sup>2</sup>/s, respectively, highlighting this disparity as the primary trigger for concealed issues like pavement delamination and swelling. Leveraging the combined capabilities of three-dimensional ground-penetrating radar (3D-GPR) and infrared thermography, this research reliably detects, identifies, and pinpoints concealed defects at three strategic locations on the steel bridge deck. The integration of these two technologies has exhibited remarkable proficiency in identifying concealed damages. Consequently, this study lays a substantial foundation for evaluating and detecting concealed distress in EA pavements atop steel bridge decks.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6490775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555507","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

Multidamage Detection of Breathing Cracks in Plate-Like Bridges: Experimental and Numerical Study 板状桥梁呼吸裂缝的多损伤检测：实验与数值研究

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-29 DOI: 10.1155/2024/8840611

Cheng Wang, Kang Gao, Zhen Yang, Jinlong Liu, Gang Wu

{"title":"Multidamage Detection of Breathing Cracks in Plate-Like Bridges: Experimental and Numerical Study","authors":"Cheng Wang, Kang Gao, Zhen Yang, Jinlong Liu, Gang Wu","doi":"10.1155/2024/8840611","DOIUrl":"https://doi.org/10.1155/2024/8840611","url":null,"abstract":"<div>\u0000 <p>Bridges may develop breathing cracks under excessive overloading vehicles, while conventional beam models are ineffective in analyzing the effect of spatial distribution of these cracks. This study proposes a data-driven detection model with the consideration of spatial distribution of breathing cracks that can detect the multiple damage locations and degrees of breathing cracks in plate-like bridges. Firstly, a 2D vehicle–bridge interaction model containing breathing cracks is established, and the damage indicator, contact point displacement variation (CPDV), is calculated using vehicle acceleration data. Next, a dataset with CPDV as the input feature is generated using the finite element method to train the CatBoost-based damage prediction model, which considers the random distribution of single and multiple cracks, as well as the influence of different vehicle speeds. Finally, by calculating the CPDV related to the actual bridge and feeding it into the trained model, the location and degree of the damage can be predicted. The numerical simulation results demonstrate that this approach can accurately detect complex crack information under various vehicle speeds and exhibits robustness against road roughness. A laboratory experiment further confirms the effectiveness, applicability, and feasibility of this method to multiple damage locations and degree of breathing cracks.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8840611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525645","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

Designing a Distributed Sensing Network for Structural Health Monitoring of Concrete Tunnels: A Case Study 设计用于混凝土隧道结构健康监测的分布式传感网络：案例研究

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-28 DOI: 10.1155/2024/6087901

Xuehui Zhang, Hong-Hu Zhu, Xi Jiang, Wout Broere, Luyuan Long

{"title":"Designing a Distributed Sensing Network for Structural Health Monitoring of Concrete Tunnels: A Case Study","authors":"Xuehui Zhang, Hong-Hu Zhu, Xi Jiang, Wout Broere, Luyuan Long","doi":"10.1155/2024/6087901","DOIUrl":"https://doi.org/10.1155/2024/6087901","url":null,"abstract":"<div>\u0000 <p>Structural health monitoring is essential for the lifecycle maintenance of tunnel infrastructure. Distributed fiber-optic sensor (DFOS) technology, which is capable of distributed strain measurement and long-range sensing, is an ideal nondestructive testing (NDT) approach for monitoring linear infrastructures. This research aims to develop a distributed sensing network utilizing DFOS for structural integrity assessment of concrete immersed tunnels. The primary innovations of this study lie in the development of a general flowchart for establishing a sensing network and obtaining reliable field data, as well as its subsequent validation through a detailed case study. Concentrated joint deformations in typical immersed tunnels, detectable by the DFOS, are key indicators of structural integrity. This study addresses crucial elements of field monitoring system design, including the selection of appropriate optical fibers or cables and the determination of vital interrogator system parameters. It also covers sensor parameter determination, installation techniques, field data collection, and postanalysis. Furthermore, this research is exemplified by a case study that illustrates the successful implementation of a distributed sensing network in an operational immersed tunnel, and monitoring data reveals cyclic structural deformations under impacts of daily tide and seasonal temperature variations. The data obtained from this network play a significant role in subsequent condition assessments of tunnel structures. The research findings contribute to the assessment of large-scale infrastructure health conditions through the application of DFOS monitoring.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6087901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525552","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

Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8 利用模形处理方法和 YOLOv8 检测复合材料层压板中的分层现象

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2024-10-26 DOI: 10.1155/2024/5740931

Mingxuan Huang, Zhonghai Xu, Dianyu Chen, Chaocan Cai, Weilong Yin, Rongguo Wang, Xiaodong He

{"title":"Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8","authors":"Mingxuan Huang, Zhonghai Xu, Dianyu Chen, Chaocan Cai, Weilong Yin, Rongguo Wang, Xiaodong He","doi":"10.1155/2024/5740931","DOIUrl":"https://doi.org/10.1155/2024/5740931","url":null,"abstract":"<div>\u0000 <p>In this study, a novel delamination detection method for composite materials is proposed through the innovative use of You Only Look Once v8 (YOLOv8), vibration analysis, and 2D continuous wavelet transform techniques. The method detects the location and size of damage more accurately than existing methods and avoids manual intervention in the detection process. Damage detection performed on the simulation dataset shows that the method is able to accurately identify the delamination location with IoU = 0.9906 and an average accuracy of 91.32%. The proposed method is then compared with the widely used YOLOv5 model, and the superior performance of the YOLOv8 model is verified, with a 37.93% improvement in training speed and 0.81% improvement in detection accuracy. In addition, an experimental dataset of four composite laminates with delamination damage is constructed. By using transfer learning, the performance of the pretrained network achieves a good precision up to 1. The method proposed in this study expands the range of tasks that can be accomplished by mode shape analysis and is very effective in real experiments.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5740931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525368","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 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