Structural Control & Health Monitoring最新文献

{"title":"Study on Antiseepage Measures for Earth-Rock Dam Reservoir in Super-Intense Karst Area: Research on Curtain Grouting Scheme for Comprehensive Seepage Prevention","authors":"Mingming Wang,&nbsp;Chunying Shen,&nbsp;Jihong Duan,&nbsp;Ming Ye,&nbsp;Qiang Xu","doi":"10.1155/stc/1319986","DOIUrl":"https://doi.org/10.1155/stc/1319986","url":null,"abstract":"<div>\u0000 <p>Constructing reservoirs in karst regions is a challenge that dam engineers around the world are very unwilling to face. The antiseepage of reservoirs is one of the urgent problems to be solved in the construction of reservoirs in karst regions. Adopting the field test method combined with advanced geological exploration instruments and survey technologies, the antiseepage measures are studied in the construction of a reservoir located within a China’s super karst region. Through a detailed hydrogeological survey, the reservoir water seeps out through the downstream primary and secondary fracture points along the limestone dissolution erosion zone through the upstream sinkholes. According to the topography, lithology, submerged area, and seepage form and direction in the reservoir area, a vertical grouting curtain is proposed to block the seepage of reservoir water to the downstream. Based on the normal water level, impermeable layer (slate) distribution, and the lowest discharge datum plane, the left, right, and bottom boundaries of the vertical curtain grouting are determined, and the maximum grouting depth reaches 131.75 m. The double curtain grouting method is proposed to reduce the construction difficulty of the super-deep grouting curtain in the intense karst area, and alongside a method is put forward to integrate the upper and lower curtains into a cohesive unit. Practical validation through the Yundong Reservoir project demonstrates the efficacy of the proposed treatment scheme, ensuring seepage-free performance for 6 years under normal water levels. The findings lay the groundwork for further studies exploring specific challenges encountered during curtain grouting construction in karst environments, which include underground karst caves, strong corrosion zones, and underground large flow and high-speed pipeline inflow.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1319986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140787","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}

{"title":"A Concrete Resistivity Method Based on a Simple Measuring Cell for Onsite Corrosion Monitoring: Study on Concrete Under Varying Conditions","authors":"J. E. Ramón,&nbsp;I. Martínez,&nbsp;J. M. Gandía-Romero,&nbsp;A. Castillo,&nbsp;M. Valcuende","doi":"10.1155/stc/5522124","DOIUrl":"https://doi.org/10.1155/stc/5522124","url":null,"abstract":"<div>\u0000 <p>Concrete resistivity (<i>ρ</i>) is commonly monitored in situ using sensors based on the rebar-disc (RDM) or four-electrode (FEM) methods. This study validates, for the first time in reinforced concrete, an innovative corrosion sensor approach (CSA) previously tested only in simulated pore solutions. The CSA uses a single embedded two-electrode sensor that also allows the corrosion rate, offering a significant advantage for structural health monitoring. CSA resistivity values were broadly consistent with those from established reference methods: 2.9% higher than the RDM and 20% lower than the two-electrode method. Larger differences were observed with the FEM, decreasing when a finite-element cell factor (103%) was applied instead of one for semi-infinite elements (208%). This trend aligns with expected differences between FEM surface resistivity and bulk values. Additionally, a simple correction factor is proposed to normalise <i>ρ</i> to the reference temperature (<i>T</i>) of 20°C, expressed as <i>1</i>/(<i>a·</i>exp((<i>b</i>)·<i>T</i>)), with <i>a</i> and <i>b</i> equal to 1.7251 and 0.027 for low-resistivity concretes and 2.4851 and 0.046 for medium- to high-resistivity concretes. A general model for the full resistivity range yielded <i>a</i> = 2.0687 and <i>b</i> = 0.036. While further research is needed to explore wider corrosion scenarios, the results highlight the potential of the CSA as a practical tool for both laboratory and in situ corrosion assessment.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5522124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135611","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}

{"title":"Robust Damage Detection and Localization Under Varying Environmental Conditions Using Neural Networks and Input-Residual Correlations","authors":"Niklas Römgens,&nbsp;Abderrahim Abbassi,&nbsp;Florian Fürll,&nbsp;Tanja Grießmann,&nbsp;Raimund Rolfes,&nbsp;Steffen Marx","doi":"10.1155/stc/3451930","DOIUrl":"https://doi.org/10.1155/stc/3451930","url":null,"abstract":"<div>\u0000 <p>This study aims to evaluate sequences of raw time series using an autoencoder structure for unsupervised damage detection and localization under varying environmental conditions (ECs). When it comes to structural health monitoring (SHM) for real-world applications, data-driven models need to improve sensitivity and robustness toward damage due to the EC-dependent variance. For systems situated outdoors, changing ECs affects the stiffness properties without causing permanent alterations to the structure. Applying data normalization strategies to consider these natural variations is not easy to conduct and is unfavorable for sensitivity regarding damage. To address these challenges, the model’s input variables are non-standardized to avoid input-related modifications and to feature a higher sensitivity toward structural changes. The autoencoder’s ability to capture structural variations caused by ECs and to handle non-standardized time series data makes it favorable for real-world applications. By quantifying the input-residual correlations, sensitivity, and robustness can be improved; no adjustments to the model have to be made. The autoencoder’s black-box nature is inspected by analyzing a linear dynamic 8DOF system and the Leibniz University Structure for Monitoring (LUMO). The neural network’s structure is identified by tracking the residual correlation. Here, a common test statistic of a whiteness test is used to find an optimal choice of the bottleneck dimension. Significantly increased robustness and sensitivity toward damage when evaluating the input-residual correlations instead of the reconstruction error is observed. To capture the temperature-dependent structural response for experimental validation, 10-min data sets of different structural temperatures are given to the neural network during training. It was derived that for damage detection, an amplitude-related normalization is inevitable due to the different excitation intensities in real life, which was carried out using input-residual correlations quantified by a Pearson coefficient. Considering the results obtained, autoencoders with non-standardized time series and input-residual correlations demonstrate a potent tool for vibration-based damage identification.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3451930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118232","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}

{"title":"Output Only Damage Detection of a Steel Truss Bridge Based on a Semisupervised BiLSTM Modeling Scheme","authors":"Tazwar Bakhtiyar Zahid,&nbsp;Shohel Rana,&nbsp;Md. Niamul Haque","doi":"10.1155/stc/5965478","DOIUrl":"https://doi.org/10.1155/stc/5965478","url":null,"abstract":"<div>\u0000 <p>The application of machine learning techniques in bridge health monitoring is gaining widespread popularity as it overcomes the problems faced by conventional methods. However, the scarcity of labeled data for damaged bridges in training the model acts as a hindrance. The present study proposes a data science–based novel approach for overcoming this hindrance using a semisupervised, output-only method for multiple-level damage identification of a steel truss bridge. The method employs sequence-to-sequence modeling of vehicle-induced vibration response only from a single sensor position. The authors have used a bidirectional long short-term memory (BiLSTM) network for damage feature extraction. A statistical distance metric tool, Kullback–Leibler divergence, has then been utilized for feature discrimination. The method’s efficiency is numerically investigated through a 3-D finite element model of a steel truss bridge based on real bridge specifications. A dynamic analysis using a moving vehicle is performed to obtain vehicle-induced accelerations. A total of 36 different damage scenarios have then been incorporated into the bridge. The effect of sensor position and performance because of variation in vehicle operation has also been investigated. The results show that the proposed approach successfully detects all the damage scenarios. The methodology’s performance has also been validated in detecting damages for the Old ADA Bridge benchmark data. The methodology successfully detected multiple damage states using a single sensor response.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5965478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117841","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}

{"title":"Application of Self-Diagnosis and Self-Repair on a Truss Prototype That Adapts to Loading Through Shape Morphing","authors":"Arka P. Reksowardojo,&nbsp;Gennaro Senatore,&nbsp;Lucio Blandini,&nbsp;Ian F. C. Smith","doi":"10.1155/stc/8827609","DOIUrl":"https://doi.org/10.1155/stc/8827609","url":null,"abstract":"<div>\u0000 <p>This paper presents experimental testing of self-diagnosis and self-repair strategies on an adaptive truss prototype that counteracts the effect of loading through shape morphing. The prototype is a simply supported spatial truss with a span of 6 m and is equipped with 12 linear actuators. The structure is designed to adapt to external loads through shape morphing—that is, by undergoing large shape changes to achieve configurations that are optimal for load-bearing. A damage event is replicated via the removal of a truss element, which simulates a loss of stiffness caused by buckling or fracture. A damage detection and localization algorithm is implemented based on the similarity evaluation of numerical and empirical redundancy matrices. Testing results demonstrate the efficacy of this method, with up to 81% and 79% accuracy for detection and localization, respectively, obtained considering all scenarios including false alarms (false positives) in the nondamaged state. For damaged states, the detection accuracy is 100% (no false negative). A self-repair strategy based on shape morphing is proposed. The structure is controlled into a shape that is optimal to carry the external load, achieving a significant stress redistribution to mitigate the effect of damage. Experimental results demonstrate that when an element of the structure is removed to simulate damage, the stress increases by up to 22% compared to the undamaged condition. This increase is fully recovered through shape adaptation. Actuator faults were also analyzed. With all actuators in operation, shape adaptation reduces stress by up to 22% under peak load (in the absence of damage). When two actuators are simulated as faulty, a stress reduction of up to 11% is still achieved, demonstrating the effectiveness of the proposed shape morphing–based control strategy.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8827609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108855","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}

{"title":"Active Learning–Enhanced Ensemble Method for Spatiotemporal Correlation Modeling of Neighboring Bridge Behaviors to Girder Overturning","authors":"Ru An,&nbsp;Mengjin Sun,&nbsp;You Dong,&nbsp;Lu Guo,&nbsp;Lei Jia,&nbsp;Xiaoming Lei","doi":"10.1155/stc/6047080","DOIUrl":"https://doi.org/10.1155/stc/6047080","url":null,"abstract":"<div>\u0000 <p>Structural health monitoring (SHM) systems are widely deployed in transportation networks, yet traditional methods often focus on individual bridges, overlooking interdependencies between neighboring structures. This study proposes an active learning–enhanced ensemble learning model to predict the tilt behavior of adjacent bridges by leveraging critical response data from multiple bridges. The ensemble model integrates gradient boosting, random forest, and Gaussian process regressors, providing both predictive means and uncertainty quantification. Active learning iteratively selects the most informative samples, improving model efficiency and reducing data requirements. The model accurately predicts vertical displacement and tilt using responses from neighboring bridges, effectively capturing spatiotemporal correlations and dynamic interactions. Active learning achieves comparable accuracy with just 50% of traditional training samples, demonstrating its efficiency. The results reveal structural interdependencies influenced by stiffness and load distribution variations. The successful prediction of tilt behavior underscores the model’s potential for real-time SHM, early overturning warnings, and enhanced bridge safety.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6047080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939418","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}

{"title":"An Energy Framework to Control Viscoelastic Semi-Active Devices in Plan-Wise One-Way Asymmetric Systems","authors":"M. De Iuliis,&nbsp;E. Miceli,&nbsp;P. Castaldo","doi":"10.1155/stc/7091316","DOIUrl":"https://doi.org/10.1155/stc/7091316","url":null,"abstract":"<div>\u0000 <p>This study proposes new strategies for the semi-active control of the dynamic response of a plan-wise asymmetrical structural system using viscoelastic devices. Different from some literature proposals, these innovative strategies are designed to be immediately interpretable, aiming to optimize the different terms of the energy balance equation through a set of closed-form analytical control algorithms to manage the properties of semi-active devices. Specifically, four algorithms have been developed to maximize the energy dissipated by the system or minimize the elastic energy, kinetic energy, and input energy. These algorithms have been tested through an extensive numerical investigation by modifying the main structural parameters of the asymmetrical system and considering 85 accelerometric input signals with different dynamic characteristics related to both far-field and near-fault records. The effectiveness of the four proposed strategies, aimed to modify the semi-active device properties, was evaluated by comparing the seismic responses of asymmetric systems, in terms of both relative displacement and energy components, with the regular configuration of semi-active devices (i.e., passive control) and other algorithms, such as “Kamagata &amp; Kobori” and “sky hook” finalized, respectively, to manage stiffness and damping extra-structural resources. The results demonstrated the effectiveness of the proposed strategies, especially, in the presence of flexible systems and high-demanding near-fault seismic events.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7091316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926030","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}

{"title":"Bolt Looseness Quantitative Visual Detection With Cross-Modal Fusion","authors":"Zhipeng Wang,&nbsp;Jiajun Ma,&nbsp;Gui Xue,&nbsp;Feida Gu,&nbsp;Ruochen Ren,&nbsp;Yanmin Zhou,&nbsp;Bin He","doi":"10.1155/stc/2282684","DOIUrl":"https://doi.org/10.1155/stc/2282684","url":null,"abstract":"<div>\u0000 <p>Intelligent bolt looseness detection systems offer significant potential for accurately promptly detecting bolt looseness. Bolt looseness detection in high-speed train undercarriages is challenging due to the low-texture surfaces of structural parts and variations of illumination and viewpoint in typical maintenance scenes. These factors hinder the quantification detection of bolt looseness using traditional 2D visual inspection methods. In this paper, we present a cross-modal fusion-based method for the quantification detection of bolt looseness in high-speed train undercarriages. We propose a cross-modal fusion approach using a cross-modal transformer, which integrates 2D images and 3D point clouds to improve adaptability to varying illumination conditions in maintenance scenes. To address geometric projection distortions caused by varying-view perspective transformations, we use the height difference between the bolt cap and the fastening plane in point clouds as the criterion for bolt loosening. The experimental results indicate that the proposed method outperforms the base-line on our dataset of 5823 annotated RGB-D images from a locomotive depot, achieving an average measurement error of 0.39 mm.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2282684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908969","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}

{"title":"Efficient Measurement of Structural Defect Depth Using Parallel Laser Line-Camera System","authors":"Chaobin Li,&nbsp;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}

{"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,&nbsp;Qingsong Chang,&nbsp;Yang Tian,&nbsp;Xin Zhang,&nbsp;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}