Structural Control & Health Monitoring最新文献

{"title":"Recovery Method of Continuous Missing Data in the Bridge Monitoring System Using SVMD-Assisted TCN–MHA–BiGRU","authors":"Jingzhou Xin,&nbsp;Xingchen Mo,&nbsp;Yan Jiang,&nbsp;Qizhi Tang,&nbsp;Hong Zhang,&nbsp;Jianting Zhou","doi":"10.1155/stc/8833186","DOIUrl":"https://doi.org/10.1155/stc/8833186","url":null,"abstract":"<div>\u0000 <p>Due to the influence of complex service environments, the bridge health monitoring system (BHMS) has to face issues such as sensor failures and power outages of data acquisition systems, leading to frequent occurrences of data missing events including continuous and discrete data missing. By comparison, the continuous data missing can cover up the time-series characteristic and make the corresponding recovery present a greater difficulty, especially for the data with a large loss rate or complicated features. To this end, this paper develops a novel signal recovery method based on the combination of successive variational mode decomposition (SVMD) and TCN–MHA–BiGRU, which is the hybrid of temporal convolutional networks (TCNs), multihead attention (MHA), and bidirectional gated recurrent unit (BiGRU). In this method, SVMD with high reliability and strong robustness is initially employed to decompose the original signal into multiple stable and regular subseries. Then, TCN–MHA–BiGRU incorporating the concept of “extraction-weighting-description of crucial features” is designed for the independent recovery of each subseries, with the ultimate recovery result derived through the linear superposition of all individual recoveries. This method not only can effectively extract the data time-frequency characteristics (e.g., nonstationarity) but also can accurately capture the data time-series characteristics (e.g., linear and nonlinear dependences) within the data. The case study and the subsequent applicability analysis grounded in the monitoring data from BHMS are employed to comprehensively evaluate the effectiveness of the proposed method. The results indicate that this method outperforms compared methods for the recovery of continuous missing data with different missing rates.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8833186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120618","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":"Physics Parameter Identification of Annular Tuned Liquid Damper for the Structure-Damper-Coupled System Using a Mechanics-Enhanced SSI Method","authors":"Wenwei Fu,&nbsp;Naiwei Kuai,&nbsp;Xin Chen,&nbsp;Shitang Ke,&nbsp;Tao Liu,&nbsp;Zhirao Shao","doi":"10.1155/stc/1495852","DOIUrl":"https://doi.org/10.1155/stc/1495852","url":null,"abstract":"<div>\u0000 <p>Tuned liquid damper (TLD) is one of the main technologies for passive control. The fundamental modal parameters of a TLD contain natural frequency and damping ratio, which are primarily related to the liquid height in the TLD device. Although the liquid height of the TLD is calibrated before installation, it is still necessary to identify the physics parameters of the TLD and the main structure during the service period to prevent the detuning of the TLD. A physics parameter identification method for the structure-damper-coupled system based on mechanics-enhanced stochastic subspace identification (SSI) is proposed in this paper, illustrated through annular TLD (ATLD). By extracting the state matrix of the first controlled mode of the main structure, the natural frequency and damping ratio of the ATLD are identified, thereby determining the liquid height of the ATLD. Numerical models of structure-ATLD-coupled systems with different degrees of freedom are constructed, and their simulated acceleration responses under different excitations are obtained. Sensitivity analysis of environmental noise is performed to verify the accuracy and robustness of the proposed parameter identification method. A dynamic test was designed for a steel chimney model to further verify the practicality of this method. The results show that when the noise level of the measurement noise is below 5.0%, the average relative error in identifying the ATLD liquid height does not exceed 10%. The identification error in the damping ratio of the structure-ATLD-coupled system will lead to a decrease in the accuracy of ATLD liquid height estimation. The proposed method can effectively identify changes in the ATLD liquid height within the optimal frequency ratio range by analyzing the experimental data from the chimney model. The proposed method can effectively estimate the modal parameters of the coupled system, providing reliable data support for evaluating the working condition of the ATLD during its service period.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1495852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120185","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":"Detection of Rupture Damage Degree in Laminated Rubber Bearings Using a Piezoelectric-Based Active Sensing Method and Hybrid Machine Learning Algorithms","authors":"Chubing Deng,&nbsp;Yunfei Li,&nbsp;Feng Xiong,&nbsp;Hong Liu,&nbsp;Xiongfei Li,&nbsp;Yi Zeng","doi":"10.1155/stc/6694610","DOIUrl":"https://doi.org/10.1155/stc/6694610","url":null,"abstract":"<div>\u0000 <p>Laminated rubber bearings may exhibit rupture damage due to factors such as temperature variations and seismic activity, which can reduce their isolation performance. Current detection methods, including human-vision inspection and computer-vision inspection, have certain limitations in accurately assessing the degree of rupture damage. This study attempts to combine the piezoelectric-based active sensing method with a machine learning algorithm to detect rupture damage in laminated rubber bearings. A series of laminated rubber bearings with varying degrees of rupture damage were fabricated, and 1440 sets of detection signals were obtained through experiments using the active sensing method. This study proposes a hybrid machine learning algorithm that integrates a one-dimensional convolutional neural network (1DCNN), long short–term memory (LSTM) network, Bayesian optimization (BO) algorithm, and extreme gradient boosting (XGB) algorithm. The algorithm involves using the 1DCNN and LSTM algorithms to extract the deep features from the wavelet packet energy spectra of the detection signals, and then employing the XGB algorithm optimized by the BO algorithm to construct the prediction model. The research results indicate that the proposed 1DCNN–LSTM–BO–XGB model achieved an accuracy value of 98.6% on the test set, outperforming the 1DCNN–LSTM (91.7%), 1DCNN (88.9%), LSTM (25.0%), XGB (90.3%), and SVM (66.7%) algorithms. Therefore, the combination of the active sensing method and machine learning algorithm shows promising application prospects in detecting the degree of rupture damage in laminated rubber bearings.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6694610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120167","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 Efficient PINN–Based Calibration Method for Mesoscale Peridynamic Concrete Models","authors":"Zhe Lin,&nbsp;Eric Gu,&nbsp;Surong Huang,&nbsp;Lei Wang","doi":"10.1155/stc/6641629","DOIUrl":"https://doi.org/10.1155/stc/6641629","url":null,"abstract":"<div>\u0000 <p>Mesoscale models are crucial for the refined analysis of material damage behaviors. However, it remains a challenging task to calibrate a mesoscale model so as to accurately simulate the mechanical behaviors (MBs) of macroscale structural components. The models may be nonlinear, involve numerous material parameters (MPs), and be large-scale. In addition, solutions to inverse problems may lack accuracy or be nonunique. A recent emerging method, physics-informed neural network (PINN), combines deep learning with physical laws to solve complex problems and significantly reduce computational costs. This paper presents an effective PINN approach for mesoscale model calibration. The approach establishes a relationship between the MPs of a mesoscale model and the MBs of structural components using PINN, with constraints based on known physical relationships. Both forward PINN (MPs as inputs and MBs as outputs) and reverse PINN (swapping inputs and outputs) models are used. Calibration is achieved efficiently by combining the forward PINN model with an optimization algorithm or directly using the reverse PINN model. Validation is performed using a mesoscale concrete model in peridynamics (PDs). The relationship between the elastic modulus of bonds in PD and MBs of components is constrained by physical laws. The datasets are generated through OpenSees analysis. The PINN method demonstrates its effectiveness, particularly with the reverse model, which is both efficient and accurate.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6641629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120182","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":"Modal and Wave Propagation Analysis of Vibration Tests on a Laboratory Building Model Before and After Damage","authors":"Chun-Man Liao","doi":"10.1155/stc/3453150","DOIUrl":"https://doi.org/10.1155/stc/3453150","url":null,"abstract":"<div>\u0000 <p>Weakened structural stiffness is often a consequence of building damage, particularly after severe events such as earthquakes, where compromised structural performance can pose significant risks. To prevent immediate structural failure, an early warning system is essential, which requires inspection of local components. This research aims to achieve that by exploring the wave propagation analysis method, specifically seismic interferometry. Previous studies have applied this method to building structures, treating them as homogeneous layers of grouped floors. By analyzing the wave travel time along the height of these layers, the fundamental period of the building was estimated. However, this approach did not account for local damage or the variability of structural components, similar to the limitations of vibration-based damage detection methods, which mainly identify global changes. Thus, the goal of this paper is to improve structural health monitoring by examining the sensitivity of wave screening, bridging the gap between nondestructive testing and vibration-based damage detection. A half-scale, seven-story building model, characterized by vertical stiffness irregularity and transverse plan asymmetry, was tested in a laboratory setting. Two vertical sensor arrays were placed near corner columns of different sizes, representing both strong and weak structural areas. These arrays recorded floor accelerations in three directions. The study confirmed the effectiveness of wave propagation analysis for detecting damage along the sensor arrays before and after the earthquake. A transmissibility damage indicator was used to correlate changes in wave velocity, providing a quantitative assessment of damage levels along the wave propagation path.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3453150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119061","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":"Automatic Water Seepage Depth Detection in Concrete Structures Using Percussion Method Combined With Deep Learning Network","authors":"Wenjie Huang,&nbsp;Kai Zhou,&nbsp;Jicheng Zhang,&nbsp;Longguang Peng,&nbsp;Guofeng Du,&nbsp;Zezhong Zheng","doi":"10.1155/stc/7386022","DOIUrl":"https://doi.org/10.1155/stc/7386022","url":null,"abstract":"<div>\u0000 <p>Water seepage in concrete can significantly degrade the durability of hydraulic concrete structures. Therefore, this paper introduces a new method that combines the percussion method with deep learning techniques to detect the depth of water seepage in concrete structures. Initially, percussion sound signals were collected for different water seepage depths. Then, the proposed one-dimensional convolutional bidirectional gated recurrent unit (BiGRU) network with wide first-layer kernel (1D-WCBGRU) classifies the percussion sound signals for different water seepage depths. The 1D-WCBGRU uses a wide first convolutional kernel to extract features directly from the original percussion signals without the need to extract features manually. Subsequently, the BiGRU is utilized to capture long short-term information from the data, thereby enhancing feature separability and improving the classification accuracy and robustness of the model. Experiments confirm that the 1D-WCBGRU exhibits excellent performance in the seepage depth detection task compared to traditional learning algorithms.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7386022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117478","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":"Assessing Feasibility and Performance of Ultrasonic Guided Wave–Based Numerical–Experimental Methodology for Debonding Monitoring of Adhesive Joints: Application to an Internal Beam of a Battery Box","authors":"Andrea Calvo-Echenique,&nbsp;Mario Sánchez,&nbsp;Emmanuel Duvivier,&nbsp;Clara Valero,&nbsp;Agustín Chiminelli","doi":"10.1155/stc/1711913","DOIUrl":"https://doi.org/10.1155/stc/1711913","url":null,"abstract":"<div>\u0000 <p>Multimaterial solutions that combine adhesively bonded composite and metallic parts are being widely proposed as lightweighting strategies to reduce environmental impact. However, the introduction of adhesive interphases in components subjected to fatigue loads is a major concern in terms of durability, reliability and maintainability. Structural health monitoring (SHM) techniques can play a key role in providing structures with self-sensing capabilities. Although the use of ultrasonic guided wave (UGW) monitoring for predicting the damage of in-service adhesive joints has been proved feasible, several challenges remain, including the generation of large and high-quality data sets and the scalability of damage detection algorithms for real-world use cases. After a wide literature review of available algorithms and simulation techniques, the simplest yet accurate methods have been selected to build a methodology that may eventually be fostered with more complex models. In this work, a numerical–experimental integrative methodology is proposed to train predictive algorithms minimizing the need for extensive experimental campaigns, by creating synthetic data sets through physics-based simulation models. Although several features have been detected as damage-sensitive, simple regression models using the root-mean-square density (RMSD) have been trained and validated as damage indicators. The feasibility of this approach has been proven in a real subcomponent with an error below 2% in the debonding length prediction, calculated as the ratio of the Euclidean distance between actual debonding and predicted debonding to the total inspection length.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1711913","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115510","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":"Dynamic Vibration Characteristics and Mitigation of the Stress-Ribbon Bridge by Using a Rail-Damper System","authors":"Kun Xu,&nbsp;HanShuo Wang,&nbsp;Meng Wang,&nbsp;Bin Liu,&nbsp;Satish Nagarajaiah,&nbsp;Qiang Han","doi":"10.1155/stc/3296513","DOIUrl":"https://doi.org/10.1155/stc/3296513","url":null,"abstract":"<div>\u0000 <p>Due to its simple and beautiful architectural appearance, the stress-ribbon bridge (SRB) has been gradually built around the world as a pedestrian or traffic bridge. However, as characterized by low bending stiffness and low damping ratio features, SRB is prone to the dynamic effects of external excitations, such as pedestrians, vehicles, and/or winds. To control the vertical vibration of the SRB, a rail-damper system is proposed in this study. In the proposed scheme, the rotation of the handrails triggered by the flexural deformation of the SRB is utilized to drive the viscous dampers installed between the adjacent handrails. The governing equations of the proposed control system are established. The key design parameters and their influences on the dynamic properties of the control system are systematically investigated. The control performances of the proposed rail-damper system are further investigated through an SRB numerical model subjected to pedestrian excitations. It is discovered that the rail-damper system can offer considerable supplemental damping to the structural modes through reasonable design, achieving satisfactory control performances. To gain the excellent effect of the proposed rail-damper system in real applications, a nondimensional rail stiffness of no less than 1000 is recommended, and the stiffness of the damper should be controlled as small as possible.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3296513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112516","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":"Performance of Real-Time Hybrid Simulation for Hunting Dampers of High-Speed Trains","authors":"Zhen Wang,&nbsp;Jiajun Xiao,&nbsp;Baoan Zhang,&nbsp;Ge Yang,&nbsp;Bin Wu,&nbsp;Xuejun Jia","doi":"10.1155/stc/4984025","DOIUrl":"https://doi.org/10.1155/stc/4984025","url":null,"abstract":"<div>\u0000 <p>One favorable solution to the issue of hunting instability of high-speed trains is to install hunting dampers. However, the nonlinearity of dampers and their interaction with a train present significant challenges in accurately analyzing the dynamic behaviors of both dampers and trains. To address these challenges, we present and investigate a real-time hybrid simulation (RTHS) for hunting dampers of high-speed trains and propose an improved two-stage adaptive time-delay compensation method to resolve its demanding delay issue. This innovative approach combines a numerical train model with a full-scale physical hunting damper, providing a versatile method for simulating and analyzing various dynamic behaviors. The train model incorporates 17 degrees of freedom and accounts for the nonlinear wheel–rail contact relationship to more faithfully represent the dynamic response of the train. A virtual RTHS platform with a loading system model has been developed. Both numerical simulations on this platform and real tests are conducted using the RTHS approach. Results demonstrate that time delays can reduce the hunting stability of a high-speed train, and the improved two-stage adaptive time-delay compensation method outperforms other comparative methods. This research reveals the feasibility and efficacy of the RTHS method for hunting dampers of high-speed trains.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4984025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121168","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":"Damage Detection in Bridge via Adversarial-Based Transfer Learning","authors":"Zhenhua Nie,&nbsp;Shenshen Xu,&nbsp;Kaijian Chen,&nbsp;Lianli Xu,&nbsp;Yizhou Lin,&nbsp;Hongwei Ma","doi":"10.1155/stc/5548218","DOIUrl":"https://doi.org/10.1155/stc/5548218","url":null,"abstract":"<div>\u0000 <p>A novel structural damage detection (SDD) method is proposed in this work, which is based on adversarial-based transfer learning to achieve the cross-domain information transfer of damage locations between numerical simulations and real bridge structures. Although the advancement of numerical modeling technology makes it accessible for relatively accurate finite element (FE) models, it is still difficult to meet the needs of practical engineering. The idea of adversarial training is introduced to enable the traditional feature extraction network to obtain the domain independent features between the numerical simulation and the real bridge structure. The dynamic response data from the numerical simulations are labeled with damage, while those from the real structure are unlabeled. To verify the effectiveness of the proposed method, we established a FE model of a simply support beam and regarded it as the benchmark model, and the target model with discrepancies from the benchmark model is obtained by quantitatively increasing the uncertainties. The results of the simulation show that the proposed method can overcome the discrepancy caused by uncertainty to a certain extent compared with the traditional method and obtain a high damage localization accuracy on the target model. In the laboratory experiment, the proposed method still achieves promising results. The primary contributions of this work are twofold: first, it delves deeper into the effectiveness of adversarial training for extracting domain-invariant features, which are crucial for structural damage identification. Second, it provides a quantitative assessment of the performance degradation of traditional methods due to modeling errors and uncertainty. Additionally, it demonstrates the significant performance enhancement achieved by the proposed method.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5548218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121170","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}