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

A Novel Nonlinear Output-Only Damage Detection Method Based on the Prediction Error of PCA Euclidean Distances Under Environmental and Operational Variations 基于环境和运行变化下 PCA 欧氏距离预测误差的新型非线性纯输出损伤检测方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-18 DOI: 10.1155/stc/4684985

Jiezhong Huang, Sijie Yuan, Dongsheng Li, Tao Jiang

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

引用次数: 0

Domain Knowledge Embedded InSAR-Based 3D Displacement Monitoring of Urban Buildings 基于领域知识嵌入式insar的城市建筑三维位移监测

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-17 DOI: 10.1155/stc/8864614

Ya-Nan Du, De-Cheng Feng, Gang Wu

引用次数: 0

Estimation of Bridge Girder Cumulative Displacement for Component Operational Warning Using Bayesian Neural Networks 基于贝叶斯神经网络的构件运行预警桥梁梁累计位移估计

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-14 DOI: 10.1155/stc/9974584

Xiaoming Lei, Zhen Sun, Ao Wang, Tong Guo, Tomonori Nagayama

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

引用次数: 0

Using Deep Learning to Estimate Vibration Comfort of Large-Scale Shake Table During Operation 基于深度学习的大型振动台运行振动舒适性评估

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-12 DOI: 10.1155/stc/6888254

Minte Zhang, Tong Guo, Yueran Zong, Weijie Xu, Chee Kiong Soh

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

引用次数: 0

Monitoring-Based Evaluation of Wind-Induced Vibration and Travel Comfort of Long-Span Suspension Bridge 基于监测的大跨度悬索桥风致振动及行驶舒适性评价

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-12 DOI: 10.1155/stc/9962003

Zhongxiang Liu, Haojun Cai, Tong Guo, Xingwang Liu, Yongtao Bai, Chunxu Qu

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

引用次数: 0

Recognition of Structural Components and Surface Damage Using Regularization-Based Continual Learning 使用基于正则化的持续学习识别结构部件和表面损伤

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-12 DOI: 10.1155/stc/6005674

Yung-I Chang, Rih-Teng Wu

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

引用次数: 0

Identification of Damping Ratios of Long-Span Bridges Using Adaptive Modal Extended Kalman Filter 基于自适应模态扩展卡尔曼滤波的大跨度桥梁阻尼比辨识

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-10 DOI: 10.1155/stc/1493319

Xiaoxiong Zhang, Rongli Luo, Jia He, Xugang Hua, Lun Yang, Xiaobin Peng, Can Yang, Zhengqing Chen

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

引用次数: 0

Bayesian Approach for Damping Identification of Stay Cables Under Vortex-Induced Vibrations 涡激振动下斜拉索阻尼辨识的贝叶斯方法

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-08 DOI: 10.1155/stc/5532528

Jiren Zhang, Zhouquan Feng, Jinyuan Dai, Yafei Wang, Xugang Hua, Wang-Ji Yan

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

引用次数: 0

Optical Flow-Based Structural Anomaly Detection in Seismic Events From Video Data Combined With Computational Cost Reduction Through Deep Learning 基于光流的视频地震事件结构异常检测与深度学习降低计算成本

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-07 DOI: 10.1155/stc/4702519

Sifan Wang, Taisei Saida, Mayuko Nishio

{"title":"Optical Flow-Based Structural Anomaly Detection in Seismic Events From Video Data Combined With Computational Cost Reduction Through Deep Learning","authors":"Sifan Wang, Taisei Saida, Mayuko Nishio","doi":"10.1155/stc/4702519","DOIUrl":"https://doi.org/10.1155/stc/4702519","url":null,"abstract":"<div>\u0000 <p>This study presents a novel approach for anomaly event detection in large-scale civil structures by integrating transfer learning (TL) techniques with extended node strength network analysis based on video data. By leveraging TL with BEiT + UPerNet pretrained models, the method identifies structural Region-of-Uninterest (RoU), such as windows and doors. Following this identification, the extended node strength network uses rich visual information from the video data, concentrating on structural components to detect disturbances in the nonlinearity vector field within these components. The proposed framework provides a comprehensive solution for anomaly detection, achieving high accuracy and reliability in identifying deviations from normal behavior. The approach was validated through two large-scale structural shaking table tests, which included both pronounced shear cracks and tiny cracks. The detection and quantitative analysis results demonstrated the effectiveness and robustness of the method in detecting varying degrees of anomalies in civil structural components. Additionally, the integration of TL techniques improved computational efficiency by approximately 10%, with a positive correlation observed between this efficiency gain and the proportion of structural RoUs in the video. This study advances anomaly detection in large-scale structures, offering a promising approach to enhancing safety and maintenance practices in critical infrastructure.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4702519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362331","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

Non-probabilistic Structural Damage Identification With Uncertainties by Phase Space–Based CNN 基于相位空间CNN的不确定性非概率结构损伤识别

IF 4.6 2区工程技术

Structural Control & Health Monitoring Pub Date : 2025-02-03 DOI: 10.1155/stc/5827324

Yue Zhong, Jun Li, Hong Hao, Ling Li

{"title":"Non-probabilistic Structural Damage Identification With Uncertainties by Phase Space–Based CNN","authors":"Yue Zhong, Jun Li, Hong Hao, Ling Li","doi":"10.1155/stc/5827324","DOIUrl":"https://doi.org/10.1155/stc/5827324","url":null,"abstract":"<div>\u0000 <p>Considering the critical role of uncertainties in structural damage detection, primarily arising from measurement errors and finite element model discrepancies, a nonprobabilistic approach based on interval analysis is proposed. This nonprobabilistic approach integrates phase space matrices with convolutional neural networks (CNNs) for damage identification. The compatibility of the phase space matrix data format with CNN allows for high sensitivity in detecting damage. Unlike probabilistic methods, this approach does not rely on specific probability distributions but considers the upper and lower bounds of uncertainties, making it highly applicable to real-world applications. The proposed method employs the phase space matrix as the input for the CNN and the elemental stiffness parameter (ESP) as the output. When accounting for uncertainties, distinct networks are developed from the upper and lower bounds of the input phase space matrix. Both the undamaged state and the state under assessment are processed through these networks. The resulting outputs enable the computation of the possibility of damage existence (PoDE) and the damage measure index (DMI), which collectively provide a comprehensive assessment of the level and probability of damage. Validation using a numerical model and experimental data confirms the effectiveness of this method in accurately determining the location and level of damage while considering uncertainties.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5827324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111159","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