Journal of Infrastructure Intelligence and Resilience最新文献_第3页

A systematic literature review of unmanned underwater vehicle-based structural health monitoring technologies 基于无人潜航器的结构健康监测技术系统文献综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-08-20 DOI: 10.1016/j.iintel.2024.100112

Joel Friesen Waldner , Ayan Sadhu

{"title":"A systematic literature review of unmanned underwater vehicle-based structural health monitoring technologies","authors":"Joel Friesen Waldner , Ayan Sadhu","doi":"10.1016/j.iintel.2024.100112","DOIUrl":"10.1016/j.iintel.2024.100112","url":null,"abstract":"<div><p>The structural health of underwater infrastructure such as bridges, dams, and pipelines are constantly degrading due to aging, fatigue, unexpected loads, and environmental wear and tear. Historically, these structures have been inspected by human divers; however, the need for safe and cost-effective monitoring has fostered the development of unmanned underwater vehicles (UUVs) capable of performing subsea surveillance. This paper provides a concise and systematic review of emerging technologies and methodologies for deploying underwater vehicles to perform inspections. Literature is classified into two main groups: advancements to UUV designs and capabilities and advancements to instrumentation for underwater structural health monitoring. After a systematic review, the existing challenges to UUV development and implementation are discussed. Finally, recommendations for future areas of research are outlined. This systematic literature survey aims to provide researchers and practitioners with a holistic outlook on the current state and future trends of UUV-based infrastructure inspection.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000318/pdfft?md5=7ec3a7a00799411ba7c6543b0bd3df9f&pid=1-s2.0-S2772991524000318-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vibration reduction technique of shield construction in water-rich karst strata 富水岩溶地层盾构施工减震技术

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-08-15 DOI: 10.1016/j.iintel.2024.100111

Jing-Rui Peng , Hua Zhou , Jing-Yi Hao , Yan-Ning Wang

{"title":"Vibration reduction technique of shield construction in water-rich karst strata","authors":"Jing-Rui Peng , Hua Zhou , Jing-Yi Hao , Yan-Ning Wang","doi":"10.1016/j.iintel.2024.100111","DOIUrl":"10.1016/j.iintel.2024.100111","url":null,"abstract":"<div><p>In shield tunneling within karst formations, the vibrational effects often impact the safety of surrounding residents and buildings. The study of construction vibration mitigation measures holds significant importance. Based on the shield tunneling project in the Huang-Shang section of the Xuzhou Metro Line 6, this paper studies the causes, propagation characteristics and influencing factors of ground vibration caused by shield construction. Three effective mitigation measures were identified: (1) Optimization adjustment of shield tunneling parameters; (2) Grouting with mixed bentonite; (3) Layout of vibration reduction boreholes. Each mitigation measure was individually tested for its impact on ground vibration. The comprehensive application of the three measures in shield tunnel construction was analyzed to assess their combined effectiveness. The integration of actual engineering measurements indicates that boreholes provide the best damping effect. Furthermore, the application of multiple mitigation measures resulted in an overall 60% reduction in ground vibration, significantly mitigating the impact on residential structures on the ground. This study provides valuable references for vibration reduction measures in other engineering projects.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000306/pdfft?md5=9651f42ae91f31613b990f48b6f67b9b&pid=1-s2.0-S2772991524000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural damage identification based on dual sensitivity analysis from optimal sensor placement 基于优化传感器位置的双重灵敏度分析的结构损伤识别

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-07-24 DOI: 10.1016/j.iintel.2024.100110

Tengrun Qi, Zhilong Hou, Ling Yu

{"title":"Structural damage identification based on dual sensitivity analysis from optimal sensor placement","authors":"Tengrun Qi, Zhilong Hou, Ling Yu","doi":"10.1016/j.iintel.2024.100110","DOIUrl":"10.1016/j.iintel.2024.100110","url":null,"abstract":"<div><p>Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277299152400029X/pdfft?md5=53f6a3a7596efb165c45cda6c77b79d6&pid=1-s2.0-S277299152400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental study on seismic behavior of RCS joints with asymmetric friction connections and slabs 非对称摩擦连接 RCS 接头和楼板地震行为的实验研究

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-07-23 DOI: 10.1016/j.iintel.2024.100109

Qi Si , Hang Li , Zhihong Pan , Junbo Jia , Qianpeng He , Yanzhang Zhu

{"title":"Experimental study on seismic behavior of RCS joints with asymmetric friction connections and slabs","authors":"Qi Si , Hang Li , Zhihong Pan , Junbo Jia , Qianpeng He , Yanzhang Zhu","doi":"10.1016/j.iintel.2024.100109","DOIUrl":"10.1016/j.iintel.2024.100109","url":null,"abstract":"<div><p>This paper introduces a new reinforced concrete column-steel beam (RCS) joint that employs asymmetric frictional connections (AFC) to improve energy dissipation and moment transfer, reducing stress concentrations within the joint’s core. Two RCS joint specimens with AFC and floor slabs were designed and tested under quasi-static loading to analyze the impact of bolt preload on seismic performance. The experimental results demonstrate that RCS joints with AFC and slabs exhibit favorable seismic behavior in terms of bearing capacity, energy dissipation, and stiffness degradation. Increasing bolt preload enhances the bearing capacity, stiffness, and energy dissipation capacity of the joints. The failure occurred at the steel beam splice connections, while only minor micro-cracks appeared in the reinforced concrete column when the joint's bearing capacity dropped below 80% of the peak load. Displacement at the column top was primarily influenced by steel beam and column deformation, with minimal contribution from joint core deformation. The use of AFC effectively reduced deformation in the joint core area, meeting seismic design code requirements for “strong columns-weak beams.”</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000288/pdfft?md5=2fcfd8ee85adbb7673fc9235b48d1ef5&pid=1-s2.0-S2772991524000288-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Life-cycle assessment for flutter probability of a long-span suspension bridge based on operational monitoring data 基于运行监测数据的大跨度悬索桥扑翼概率生命周期评估

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-07-15 DOI: 10.1016/j.iintel.2024.100108

Junfeng Tan , Xiaolei Chu , Wei Cui , Lin Zhao

{"title":"Life-cycle assessment for flutter probability of a long-span suspension bridge based on operational monitoring data","authors":"Junfeng Tan , Xiaolei Chu , Wei Cui , Lin Zhao","doi":"10.1016/j.iintel.2024.100108","DOIUrl":"10.1016/j.iintel.2024.100108","url":null,"abstract":"<div><p>Accurate evaluation of flutter probability is of paramount importance in the design of long-span bridges. In current engineering practice, at the design stage, flutter critical wind speed is usually estimated by the wind tunnel test with section model or aeroelastic model, which is sensitive to modal frequencies and damping ratios. After construction, structural properties of existing structures will change with time due to various factors, such as structural deteriorations and periodic environments. The structural dynamic properties, such as modal frequencies and damping ratios, cannot be considered as the same values as the initial ones, and the deteriorations should be included when estimating the life-cycle flutter probability. This paper proposes an evaluation framework to assess the life-cycle flutter probability of long-span bridges considering the deteriorations of structural properties, based on field monitoring data. Fast Bayesian approach is employed for modal identification of a suspension bridge with the center span of 1650 m, and the field monitoring data during 2010–2015 is analyzed to determine the deterioration functions of modal frequencies and damping ratios, as well as their inter-seasonal fluctuations. According to the historical trend, the long-term structural properties can be predicted. Consequently, the probability distributions of flutter critical wind speed for each year in the long term are calculated, conditionally based on the predicted modal frequencies and damping ratios.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000276/pdfft?md5=b92f5e55353a18a4201520ef266a88c8&pid=1-s2.0-S2772991524000276-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancement of data-driven SHM: A research paradigm on AE-based switch rail condition monitoring 推进数据驱动的 SHM：基于 AE 的道岔轨道状态监测研究范例

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-07-07 DOI: 10.1016/j.iintel.2024.100107

Lu Zhou , Si-Xin Chen , Yi-Qing Ni , Xiao-Zhou Liu

{"title":"Advancement of data-driven SHM: A research paradigm on AE-based switch rail condition monitoring","authors":"Lu Zhou , Si-Xin Chen , Yi-Qing Ni , Xiao-Zhou Liu","doi":"10.1016/j.iintel.2024.100107","DOIUrl":"10.1016/j.iintel.2024.100107","url":null,"abstract":"<div><p>The past ten years have witnessed the tremendous progress of structural health monitoring applications in civil infrastructures. This is particularly embodied in railway engineering. The increasing train speed brings greater challenges to safety and ride comfort, and the primary theme of maintenance has been gradually altered from offline inspection to online monitoring. Rail operators must get an in-time warning of potential structural defects before critical failure takes place. It is more favourable that the rail operators can take hold of the real-time status of the key components and infrastructures in railway systems. This paper summarizes a long-term research series by the authors’ research team on online monitoring of rail tracks at turnout areas utilizing acoustic emission-based sensing technique, and more importantly, successively advancing signal processing methods and data-driven analysing frameworks, covering Bayesian inference, convolutional neural networks, transfer learning and task similarity analysis. The proposed algorithms tackle noise interference brought by wheel-rail impacts, great uncertainties in an open environment, and insufficiency of monitoring data, and realize comprehensive monitoring of rail tracks in turnout areas from basic crack detection to regressive condition assessment step-by-step.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000264/pdfft?md5=ea2debef5f66f941ca83ceac7ba1d133&pid=1-s2.0-S2772991524000264-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating models of civil structures in digital twins: State-of-the-Art and challenges 在数字孪生中整合土木结构模型：技术现状与挑战

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-06-10 DOI: 10.1016/j.iintel.2024.100100

{"title":"Integrating models of civil structures in digital twins: State-of-the-Art and challenges","authors":"","doi":"10.1016/j.iintel.2024.100100","DOIUrl":"10.1016/j.iintel.2024.100100","url":null,"abstract":"<div><p>Software systems monitoring civil structures over their lifetime are exposed to the risk of aging much faster than the structures themselves. This risk can be minimized if we use models describing the structure, geometry, processes, interaction, and risk assessment as well as the data collected over the lifetime of a civil structure. They are considered as a unity together with the civil structure. These model-based systems constitute a digital twin of such a civil structure, which through appropriate operative services remain in permanent use and thus co-evolve with the civil structure even over a long-lasting lifetime. Even though research on digital twins for civil structures has grown over the last few years, digital twin engineering with heterogeneous models and data sources is still challenging. Within this article, we describe models used within all phases of the whole civil structure life cycle. We identify the models from the computer science, civil engineering, mechanical engineering, and business management domains as specifically relevant for this purpose, as they seem to cover all relevant aspects of sustainable civil structures at best, and discuss them using a dam as an example. Moreover, we discuss challenges for creating and using models within different scenarios such as improving the sustainability of civil structures, evaluating risks, engineering digital twins, parallel software and object evolution, and changing technologies and software stacks. We show how this holistic view from different perspectives helps overcome challenges and raises new ones. The consideration from these different perspectives enables the long-term software support of civil structures while simultaneously opening up new paths and needs for research on the digitalization of long-lasting structures.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000197/pdfft?md5=ebc59ea98f23e143c52114afbe83c226&pid=1-s2.0-S2772991524000197-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141408335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum to “Generative adversarial network for predicting visible deterioration and NDE condition maps in highway bridge decks” [J. Infrastruct. Intell. Resilience 2 (2023) 100042] 对 "用于预测公路桥面可见劣化和无损检测条件图的生成对抗网络 "的勘误 [J. Infrastruct. Intell. Resilience 2 (2023) 100042]

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-06-01 DOI: 10.1016/j.iintel.2024.100099

Amirali Najafi , John Braley , Nenad Gucunski , Ali Maher

引用次数: 0

Recognition and classification of microscopic fatigue fracture images of high-strength bolt using deep learning methods 利用深度学习方法识别和分类高强度螺栓的微观疲劳断裂图像

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-04-20 DOI: 10.1016/j.iintel.2024.100097

Shujia Zhang , Liang Zhang , Guoqing Wang , Zichun Zhou , Honggang Lei

{"title":"Recognition and classification of microscopic fatigue fracture images of high-strength bolt using deep learning methods","authors":"Shujia Zhang , Liang Zhang , Guoqing Wang , Zichun Zhou , Honggang Lei","doi":"10.1016/j.iintel.2024.100097","DOIUrl":"10.1016/j.iintel.2024.100097","url":null,"abstract":"<div><p>The fracture surface of high-strength bolt after fatigue fracture contains a lot of information, such as the location of stress concentration and the distribution of fatigue cracks. In this study, a large number of scanning electron microscope (SEM) images of fatigue fracture surface of broken high-strength bolt were identified and classified using the method of deep learning. At the beginning, a data set of SEM images containing 1556 fatigue fractures of high-strength bolts was prepared. Then, three convolutional neural networks, VGG16, ResNets50 and MobileNets, were used to recognize and classify the images in the dataset. In this process, part of the convolution layer of ResNets50 was extracted for visualization. At the same time, the Loss-Epoch curves, accuracy, recall and confusion matrices of the three networks were derived to evaluate the nets. Finally, the network with the highest accuracy was selected to adjust the parameters to further improve the accuracy of the classification. It was found that the three nets can complete the classification of these images. MobileNets had the best performance for this classification task, and the accuracy rate after adjusting the parameters has reached 86.76%. For some images with obvious features, the recall rate of classification had reached 100%. However, images from the same fatigue area were prone to a small amount of confusion. Finally, the feature map of the network would become more abstract with the deepening of the network, and the features of the image concerned by each convolution layer were also different.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000161/pdfft?md5=cd441d727cd921753848e40590210bf2&pid=1-s2.0-S2772991524000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140784870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Random bridge generator as a platform for developing computer vision-based structural inspection algorithms 将随机桥梁生成器作为开发基于计算机视觉的结构检测算法的平台

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-04-17 DOI: 10.1016/j.iintel.2024.100098

Haojia Cheng , Wenhao Chai , Jiabao Hu , Wenhao Ruan , Mingyu Shi , Hyunjun Kim , Yifan Cao , Yasutaka Narazaki

{"title":"Random bridge generator as a platform for developing computer vision-based structural inspection algorithms","authors":"Haojia Cheng , Wenhao Chai , Jiabao Hu , Wenhao Ruan , Mingyu Shi , Hyunjun Kim , Yifan Cao , Yasutaka Narazaki","doi":"10.1016/j.iintel.2024.100098","DOIUrl":"10.1016/j.iintel.2024.100098","url":null,"abstract":"<div><p>Recent advances in computer vision algorithms have transformed the bridge visual inspection process. Those algorithms typically require large amounts of annotated data, which is lacking for generic bridge inspection scenarios. To address this challenge efficiently, this research designs, develops, and demonstrates a platform that can provide synthetic datasets and testing environments, termed Random Bridge Generator (RBG). The RBG produces photo-realistic 3D synthetic environments of six types of bridges randomly, automatically, and procedurally. Following relevant standards and design practice, the RBG creates random cross-sectional shapes, converts those shapes into bridge components, and assembles the components into bridges. The effectiveness of the RBG is demonstrated by producing a dataset (RBG Dataset) containing 10,753 images with pixel-wise annotations, rendered in 250 different synthetic environments. Significant diversity of the photo-realistic bridge inspection environments has been achieved, while all structural components strictly conform to the definitions derived from structural engineering documents. The use of the RBG dataset has been demonstrated by training a deep semantic segmentation algorithm with 101 convolutional layers, showing successful segmentation results for both major and minor structural components. The developed RBG is expected to enhance the level of automation in bridge visual inspection process. The Python code for RBG is made public at: <span>https://github.com/chenghaojia2323/Random-Bridge-Generator.git</span><svg><path></path></svg>.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000173/pdfft?md5=58700757be314ae33cab0ac0f3e2707a&pid=1-s2.0-S2772991524000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0