Structural Control & Health Monitoring最新文献

{"title":"Real-Time Machine Learning Ship and Bridge Pier Collision Prediction to Enhance Construction Health and Safety","authors":"Abdul Haq,&nbsp;Syed Saad,&nbsp;Kumeel Rasheed,&nbsp;Hamza Jamal,&nbsp;Syed Ammad,&nbsp;Abdur Rehman Nasir","doi":"10.1155/stc/5568505","DOIUrl":"https://doi.org/10.1155/stc/5568505","url":null,"abstract":"<p>Ship–bridge pier collisions signify a serious risk to structural integrity and construction health and safety during bridge construction phases where structural redundancy is limited. This study develops a hybrid finite element–machine learning (FE–ML) replacement framework for rapid prediction of collision consequence severity under different maritime and environmental conditions. Nonlinear finite element (FE) simulations were conducted across a parametric domain defined by ship tonnage, velocity, collision angle and pier geometric characteristics to quantify structural responses. It includes displacement, peak impact force, stress distribution, energy absorption and acceleration behaviour. FE results demonstrate strong nonlinear dependence of structural response on kinetic energy transfer. The increased ship speed from 5 to 20 m/s produced approximately fourfold growth in pier displacement from 0.08 to 0.30 m for a 16,000-ton vessel. Moreover, the peak impact force increased from 0.8 MN to 3.5 MN under the same tonnage range. The large tonnage collision scenarios (50,000 tons) generated forces up to 5.7 MN along with stress concentrations approached 90 MPa at the pier base. Energy absorption capacity increased substantially from 180 kJ for moderate impacts to 650 kJ under severe collision conditions. This confirms the dominance of velocity and vessel mass in governing structural damage mechanisms. ML models, random forest regression and feedforward neural networks (NNs) were trained using FE-generated datasets to enable rapid consequence prediction. Baseline evaluation using an 80/20 train–test split yielded strong predictive capability with coefficients of determination of 0.93 (random forest regressor [RFR]) and 0.95 (NN) during training (0.89) and testing (0.91). Expanded fivefold cross-validation on a synthesized dataset (<i>N</i> = 50,000) produced near-unity regression accuracy and achieved mean <i>R</i>² values of 0.9980 for RFR and 0.9988 for NN with minimal prediction error dispersion. The proposed FE–ML framework enables near real-time estimation of ship collision consequence severity and establishes a direct linkage between navigation parameters and structural response demand. The results support implementation within construction health and safety management systems for rapid hazard screening, protective design optimization and proactive maritime traffic control.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5568505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708206","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":"Deflection Prediction of Track-Slab Construction of High-Speed Railway Bridge Based on Impact Vibration Testing and Flexibility Identification","authors":"Ze Chen,&nbsp;Siqi Jia,&nbsp;Qi Xia,&nbsp;Jian Zhang","doi":"10.1155/stc/2152866","DOIUrl":"https://doi.org/10.1155/stc/2152866","url":null,"abstract":"<p>Line shape control is the most critical step in the construction of high-speed railway bridges. The deflection of long-span bridges (such as cable-stayed bridges) is more complex as a multipoint elastic-supported system, especially during track slab construction. Accurately predicting the line shape of the main girder during the track-slab paving process is an essential prerequisite for ensuring the track regularity of the high-speed railway. Therefore, this article proposes a method for identifying flexibility and predicting deflections of a multipoint elastic-supported structure based on impact vibration testing. First, the cable-stayed bridge model is transformed into a multipoint elastic-supported model based on mechanics, and its vibration equation is derived. The modal parameters of the vibration equation are solved, and the flexibility matrix of the multipoint elastic-supported structure model is further deduced. Subsequently, the proposed theoretical method is verified by the finite element model. Finally, the high-speed railway cable-stayed bridge, taking the main girder deflection during the track-slab paving process as an example, is predicted for three cases: no-track slab, two-track slabs, and full-track slabs. The predicted deflection and measured deflection are generally consistent, with an average error of only 6%. The proposed method can be used to determine the deflection of track-slabs paving and improve construction efficiency.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2152866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708105","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":"Intelligent Prediction of Pavement Structural Degradation Using a Multimodel Ensemble Learning Approach","authors":"Kaiwen Lei,&nbsp;Bingyan Cui,&nbsp;Mian Zhang,&nbsp;Rui Mao,&nbsp;Yongzuo Du,&nbsp;Xingyu Gu,&nbsp;Zhen Liu","doi":"10.1155/stc/6132295","DOIUrl":"https://doi.org/10.1155/stc/6132295","url":null,"abstract":"<p>Accurate prediction of the long-term structural response of pavement systems is critical for performance evaluation and intelligent maintenance within the context of structural health monitoring. This study proposes an automated machine learning framework to predict the time-dependent evolution of pavement rutting and deflection using full-scale monitoring data collected from the RIOHTrack facility between 2016 and 2023. A high-dimensional time-series dataset was constructed to integrate loading, environmental, and material parameters. The AutoGluon platform was employed to perform multimodel comparison and develop a weighted stacked ensemble model incorporating XGBoost, random forest, and neural networks. The ensemble achieved superior generalization performance, with <i>R</i>² values of 0.9821 and 0.9817 for rutting and deflection prediction, respectively. SHAP analysis revealed that cumulative load, service time, and temperature were dominant factors influencing structural response. Long-term forecasting indicated cyclic yet progressive degradation patterns, consistent with the measured temperature sensitivity and mechanical evolution of the pavement structure. The proposed approach demonstrates the potential of automated ensemble learning for structural health monitoring and long-term performance assessment of pavement systems.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6132295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708104","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":"Structural Damage Identification for Bridge Using Moving Phase Space Reconstruction With a Single Sensor","authors":"Zhenhua Nie,&nbsp;Shaogeng Chen,&nbsp;Ruofan Gao,&nbsp;Jing Zhang,&nbsp;Ye Yuan,&nbsp;Hongwei Ma","doi":"10.1155/stc/3681127","DOIUrl":"https://doi.org/10.1155/stc/3681127","url":null,"abstract":"<p>Existing vibration-based damage identification methods face significant challenges in practical engineering applications due to the reliance on a large number of sensors. To overcome the limitation, this paper proposes a novel structural damage identification method based on moving phase space reconstruction, which can use only a single sensor for effective damage identification. The proposed method introduces the concept of a subphase space and adopts an improved damage indicator to analyze the degree of topological change of phase trajectories at different time points. The parameters required for this method are completely determined by the vibration signal, without the need for detailed geometric or material data of the structure. To verify the effectiveness and reliability of the proposed method, the numerical simulation of a simply supported beam is carried out, and then the experimental verification is conducted on a hollow box girder. The results show that the proposed method can successfully identify and locate structural damage using only the vibration data of a single sensor and can effectively evaluate the degree of topological change in phase trajectories over time.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3681127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708008","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":"Bridge Health State Assessment and Multiscale Deterioration Modeling: Methods, Challenges, and Future Prospects","authors":"Kangliying Yin,&nbsp;Hanbin Luo,&nbsp;Yixiao Shao,&nbsp;Wenli Liu","doi":"10.1155/stc/3578303","DOIUrl":"https://doi.org/10.1155/stc/3578303","url":null,"abstract":"<p>Bridge deterioration modeling plays a crucial role in infrastructure maintenance and lifespan prediction. Current methodologies evolve along three axes: (1) Mechanism-driven models, which leverage structural mechanics and material degradation theories to provide detailed insights into the physical processes underlying deterioration, constrained by computational intensity; (2) probabilistic frameworks, which are used to quantify degradation uncertainties, effective for long-term reliability yet insensitive to anomalies; and (3) data-driven models, which are used for high-dimensional pattern mining, limited by data dependency and interpretability barriers. The hybrid intelligence paradigm emerges as a transformative solution, integrating physical laws, stochastic processes, and machine learning. This review systematically evaluates contemporary techniques across computational efficiency, predictive robustness, and engineering applicability. In addition, comprehensive structural health monitoring of bridges is advancing through the investigation of deterioration mechanisms, the application of nondestructive damage detection tools, and the exploration of emerging technologies such as AI-based tools, data fusion integrated with digital twin systems. Priority innovations should focus on (1) developing resilient data processing methods, (2) novel multisensor joint reconstruction algorithms that can effectively mitigate simultaneous data loss, (3) creating prescriptive analytics systems that synchronize real-time structural responses with probabilistic multihazard simulations, and (4) incorporating attention mechanisms and robust recursive algorithms into time-series models to better capture long-term dependencies and mitigate error accumulation.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3578303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707941","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":"Machine Vision–Based Defect Detection and Quantitative Assessment for Pavement Maintenance","authors":"Chenlang Zhou,&nbsp;Yiqiang Sun,&nbsp;Shijie Zhou,&nbsp;Xianjie Deng,&nbsp;Puzhong Wang","doi":"10.1155/stc/2744286","DOIUrl":"https://doi.org/10.1155/stc/2744286","url":null,"abstract":"<p>An efficient detection and quantification of pavement defects is a significant challenge for intelligent road maintenance; nevertheless, current methodologies exhibit poor accuracy, inadequate lightweight characteristics, and substantial 2D quantification errors. This paper proposes a collection of efficient and lightweight methods. Initially, the lightweight detection network YOLO-CGR is established, using the convolutional block attention module (CBAM) into the backbone network, alongside the integration of self-developed modules C2f with GSConv (C2f-GS) and ResBlock with efficient multiscale attention (Res-EMA). The values of [email protected] and [email protected]:0.95 on the RDD2022 dataset reach 72% and 44.9%, respectively, outperforming YOLOv8n by 4.8% in both metrics, with only a 0.6M increase in the number of parameters. The DeepLabv3+ network architecture is improved for the crack segmentation task: the encoding phase uses the lightweight backbone MobileNetV2 and cascade atrous spatial pyramid pooling (CASPP); the decoder incorporates GhostConv and the proprietary upsampling feature fusion (UFF) module, resulting in enhanced Intersection over union (IoU) and class pixel accuracy (CPA) values of the algorithm. The algorithm’s IoU and CPA values are increased to 73.49% and 82.55%, respectively, while the computational load is diminished by 31%. Ultimately, the crack segmentation results are utilized to implement the double-layer edge protection (DLEP)–Zhang-Suen refinement technique, which enhances the cracks, delineates the branching cracks, and optimizes the trajectory of each crack, achieving a precise measurement with an average relative error of 3.11%. This study presents a comprehensive solution encompassing defect detection, segmentation, and quantification, significantly outperforming existing methods in detection efficiency, segmentation accuracy, and quantification reliability, while providing improved technological support for intelligent pavement maintenance.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2744286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707912","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":"Review of Digital Twin in Structural Dynamics to Improve Condition Monitoring","authors":"Solomon Alemneh Adimass,&nbsp;Arkadiusz Żak","doi":"10.1155/stc/2777297","DOIUrl":"https://doi.org/10.1155/stc/2777297","url":null,"abstract":"<p>Digital twins (DTs) are increasingly investigated for condition monitoring (CM), with validation reported primarily in numerical, laboratory and limited pilot studies. Conventional structural health monitoring (SHM) methods offer limited flexibility and predictive capability, with delayed insights reported in many infrastructure applications. DTs are commonly defined as frameworks that update virtual replicas using continuous data streams. This review presents a structured overview of current DT methodologies and their reported applications in structural CM within structural dynamics. The review focuses on critical infrastructure, including bridges, offshore platforms and aerospace systems. It also identifies challenges in large-scale DT implementation, including computational demands, data integration complexity and cybersecurity concerns. When multiple environmental and operational hazards interact, response-based condition inference can become nonunique and ambiguous. Several studies report improved predictive accuracy for selected AI-enhanced DT frameworks under controlled numerical and laboratory conditions, while long-term field validation remains scarce. Evidence supporting sustained large-scale or long-term field performance remains limited. The review synthesises reported developments in AI-enabled DTs for structural dynamics and distinguishes demonstrated performance from conceptual or proposed benefits. It highlights reported capabilities and limitations, including data dependence, limited generalisability and the absence of standardised benchmarking frameworks.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2777297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708398","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 Vibration Control Paradigm for Stay Cable Vibration: A Hybrid Imitation and Deep Reinforcement Learning Approach","authors":"Yi-Ang Zhang,&nbsp;Songye Zhu","doi":"10.1155/stc/5588873","DOIUrl":"https://doi.org/10.1155/stc/5588873","url":null,"abstract":"<p>Although classical active control techniques demonstrate superior performance in controlling structural vibrations, deriving the optimal control strategy often faces challenges due to the difficulties in accurately modeling complex dynamic systems in practical scenarios. Learning-based control methods eliminate such a requirement by directly learning control strategies from structural behavior. However, past studies on learning-based control algorithms primarily focused on their applicability without guaranteeing optimal control performance, resulting in a performance gap between learning-based vibration control and model-based optimal control. A large number of samples required in the learning process present another practical issue in real implementations. In response, this study presents a groundbreaking learning-based control framework, which combines imitation learning (IL) and deep reinforcement learning (DRL) to mitigate structure vibrations. This approach involves initially training a deep neural network controller through behavior cloning of an extremely simple control strategy, followed by fine-tuning using model-free DRL. The framework’s feasibility and effectiveness are extensively examined in simulations by using a verified numerical model of a full-scale stay cable system with an active damper. The control performance of the proposed model-free method closely approaches that of the model-based full-state feedback controller, regardless of the number of observation states or measurement noises, and achieves better sample efficiency than other state-of-the-art model-free DRL algorithms. This innovative approach to structure vibration mitigation can revolutionize efficiency in practice, and its unparalleled effectiveness makes it a promising alternative in controlling stay cables or other high-degree-of-freedom structures.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5588873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708356","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":"Multiscale Segmentation and Quantitative Grading Detection of Subway Tunnel Crack Images","authors":"Yaodong Wang,&nbsp;Chenhao Guo,&nbsp;Wendi Jin,&nbsp;Yang Lei,&nbsp;Hongmei Shi,&nbsp;Liqiang Zhu,&nbsp;Baoqing Guo,&nbsp;Zujun Yu","doi":"10.1155/stc/8815323","DOIUrl":"https://doi.org/10.1155/stc/8815323","url":null,"abstract":"<p>While crack detection technologies for subway tunnels have diversified, systematic width-based classification remains underexplored. We developed a tunnel inspection system with high-resolution area array cameras, enabling high-definition crack imaging. Systematically classified by millimeter-scale criteria and annotated with pixel-level precision, the dataset established a multiscale crack database encompassing diverse dimensional features. Building on this, an encoder–decoder neural network optimized for multiscale crack segmentation was proposed, achieving enhanced recognition accuracy across crack dimensions. Experimental results demonstrated the method’s significant advantages over conventional models. The method achieved 91.8% pixel accuracy (PA) for cracks with widths of 10 pixels or more, 80.05% PA for cracks spanning 6–9 pixels, and maintained 61.94% PA for cracks as narrow as 1-2 pixels. These metrics underscore the framework’s robustness in supporting precision maintenance protocols for underground infrastructure.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8815323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585002","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":"Accelerated Bayesian Inference of Structural Damage Detection Using a Convolutional Neural Network Surrogate Model","authors":"Rongrong Hou,&nbsp;Linwang Che,&nbsp;Jialin Hou,&nbsp;Yuequan Bao,&nbsp;Yong Xia","doi":"10.1155/stc/1855588","DOIUrl":"https://doi.org/10.1155/stc/1855588","url":null,"abstract":"<p>Performing Bayesian inference of structural damage detection is always computationally intractable, as the corresponding posterior probability density function (PDF) is commonly configured with hundreds/thousands of parameters. For Markov Chain Monte Carlo (MCMC) methods, thousands of likelihood calls are required for stochastic exploration of the parameter space, while evaluation of the likelihood function requires complex forward modeling for each set of structural parameters explored during the sampling process. This study proposes an accelerated Bayesian inference (ABI) algorithm for structural damage identification based on a convolutional neural network (CNN)-based surrogate model. As an emulator for the likelihood function, the CNN is constructed using samples from the prior distribution of damage parameters, and the weighted sampling is employed to select uniformly distributed samples in the parameter space for model training. The Metropolis–Hasting (MH) algorithm with this surrogate model is then integrated into the delayed acceptance framework, in which the surrogate model behaves as an approximate model to generate easily computed proposals transferred to the exact model, thus reducing the computational burden imposed by repeated evaluations of the computationally expensive likelihood. Considering the non-negligible bias of the surrogate model, the enhanced error model is used to adaptively correct the surrogate model using online posterior samples. An experimental three-story frame and a numerical cable-stayed bridge are utilized to verify the effectiveness of the proposed method. Compared to traditional sampling methods, the computational cost of damage detection can be reduced by up to 75%, and at the same time the damage identification accuracy has been improved, especially for large-scale complex structures. Although the proposed algorithm is developed for a particular structural damage identification problem, it can be extended to all other models with a costly likelihood for accelerating uncertainty quantification. Moreover, the proposed CNN surrogate model can be incorporated into other sampling-based methods.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1855588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147579775","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}