Journal of Infrastructure Intelligence and Resilience最新文献_第2页

A survey of generative models for image-based structural health monitoring in civil infrastructure 基于图像的民用基础设施结构健康监测生成模型综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100138

Gi-Hun Gwon, Hyung-Jo Jung

{"title":"A survey of generative models for image-based structural health monitoring in civil infrastructure","authors":"Gi-Hun Gwon, Hyung-Jo Jung","doi":"10.1016/j.iintel.2025.100138","DOIUrl":"10.1016/j.iintel.2025.100138","url":null,"abstract":"<div><div>Accurately assessing and monitoring the condition of structures is essential for ensuring the safety and integrity of civil infrastructure. Over the past decade, image-based structural health monitoring technologies have emerged as powerful tools to enhance efficiency and improve the objectivity of structural evaluations. The integration of deep learning technologies with these monitoring systems has significantly improved the efficiency and reliability of structural condition diagnostics. Of particular interest are specifically Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, which have gained increasing attention due to their versatility in data generation and ability to address fundamental challenges in structural monitoring. While image-based structural health monitoring encompasses both damage detection and structural response measurements, this review primarily focuses on local-level monitoring applications such as damage detection, where generative models have demonstrated particular effectiveness in addressing challenges like limited data availability and environmental variations. This paper provides a comprehensive analysis of these generative models, examining their underlying concepts, mechanisms, and applications in image-based structural health monitoring. Key applications are reviewed, including structural damage detection, data augmentation for training, and emerging areas such as image quality enhancement and domain generalization. Our analysis presents the current state of generative models in structural monitoring, identifying critical challenges and promising future research directions. This systematic review serves as a foundational resource for researchers and practitioners in the field, offering insights into current achievements and potential advancements.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776268","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

Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete 物理训练的人工智能框架，以检测氯化物引起的混凝土降解

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100139

Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu

{"title":"Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete","authors":"Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu","doi":"10.1016/j.iintel.2025.100139","DOIUrl":"10.1016/j.iintel.2025.100139","url":null,"abstract":"<div><div>Numerous critical infrastructures in the United States, including bridges, dams, and nuclear plants, are aging and prone to concrete degradation, compromising their performance and structural integrity. One of the leading causes of degradation is chloride-induced corrosion, where chloride ions diffuse into the concrete, leading to reinforcement corrosion, spalling, and cracking. Detecting chloride degradation at an early stage is crucial for ensuring the safety of these vital structures. However, the visible signs of degradation, such as spalling and cracking, often appear only after significant damage has occurred. Degradation occurs gradually over many years, making it impractical to collect real-time non-destructive testing (NDT) data over extended periods while allowing the structure to continue deteriorating. To overcome this challenge, an integrated structural health monitoring framework is proposed that combines advanced finite element modeling, sensor data, and deep learning techniques. This framework follows a multi-step approach to simulate chloride degradation over the service life of the structure. Subsequently, finite element analyses are performed to numerically simulate non-destructive testing at various stages of degradation to generate corresponding sensor data. By leveraging these simulated data and insights, a physics-driven artificial intelligence framework is developed. The proposed framework offers a state-of-the-art solution to mitigate the challenges associated with long-term degradation monitoring by utilizing high-fidelity simulations and data-driven techniques to achieve detection of chloride-induced concrete damage.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563491","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

Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions 基于贝叶斯混合因子的结构损伤检测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-06 DOI: 10.1016/j.iintel.2025.100140

Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue

{"title":"Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions","authors":"Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue","doi":"10.1016/j.iintel.2025.100140","DOIUrl":"10.1016/j.iintel.2025.100140","url":null,"abstract":"<div><div>Variations of structural dynamic parameters (e.g., frequencies and damping ratios) can be caused by potential structural damages and environmental effects (e.g., temperature, humidity). It is of critical importance to distinguish them for a reliable vibration-based damage detection. A variational Bayesian mixture of factor analyzers (VB-MFA) is proposed in this paper for the probabilistic modeling of measured natural frequencies. It contains multiple factor analyzers to accommodate the nonlinear effect of environmental factors on the natural frequencies. The variational Bayes with automatic relevance determination prior empowers it to automatically determine the number of analyzers and the dimension of latent factors in each analyzer. In addition, the predictive marginal likelihood of natural frequencies is proposed as a damage index, which naturally considers the uncertainties in latent factors and estimated parameters. The method is verified in two case studies: a laboratory eight-story shear-type building model and the Z24-Bridge, both subjected to temperature variations. It shows that better performance has been achieved comparing to the conventional factor analysis and mixture of factor analyzers. The VB-MFA is capable to model the nonlinear effect of environmental effect on natural frequencies, and improves the accuracy of vibration-based structural damage detection.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552763","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

Few-shot learning with large foundation models for automated segmentation and accessibility analysis in architectural floor plans 在建筑平面图中进行自动分割和可访问性分析的大型基础模型的少量学习

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-12-17 DOI: 10.1016/j.iintel.2024.100137

Haolan Zhang, Ruichuan Zhang

{"title":"Few-shot learning with large foundation models for automated segmentation and accessibility analysis in architectural floor plans","authors":"Haolan Zhang, Ruichuan Zhang","doi":"10.1016/j.iintel.2024.100137","DOIUrl":"10.1016/j.iintel.2024.100137","url":null,"abstract":"<div><div>This paper presents a novel approach for extracting accessibility features from 2D raster floor plans by integrating few-shot learning techniques with the Segment Anything Model (SAM) and GPT-4. The proposed method addresses the limitations of existing deep learning-based floor plan analysis, which often require extensive annotated datasets and struggle with the variability of raster floor plans. Furthermore, there is a lack of research on extracting accessibility features from 2D raster floor plans, which remain one of the most common formats for storing architectural plans post-design and construction. Our approach, GPT-integrated Multi-object Few-shot SAM (GMFS), leverages similarity maps and cluster-based point sampling to generate accurate visual prompts for SAM, enabling the segmentation of rooms and doors using only five reference samples. The segmented masks are then classified using GPT-4, enhancing the semantic richness of the floor plan analysis. We validated GMFS using the CubiCasa and Rent3D datasets, demonstrating impressive performance in segmentation and classification. A detailed case study further showcased the practical application of our approach in calculating accessible means of egress and wheelchair clear space, which are critical features for accessibility compliance. The results highlight the effectiveness and adaptability of our approach in real-world scenarios, underscoring its potential to improve building accessibility and safety analysis in the architecture, engineering, and construction (AEC) industry.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642796","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

Intelligent control of structural vibrations based on deep reinforcement learning 基于深度强化学习的结构振动智能控制

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-12-15 DOI: 10.1016/j.iintel.2024.100136

Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu

{"title":"Intelligent control of structural vibrations based on deep reinforcement learning","authors":"Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu","doi":"10.1016/j.iintel.2024.100136","DOIUrl":"10.1016/j.iintel.2024.100136","url":null,"abstract":"<div><div>This paper explores the application of Deep Reinforcement Learning (DRL) in structural vibration control, aiming to achieve effective control of the dynamic response of building structures during natural disasters such as earthquakes. A DRL-based control strategy is proposed, and dynamic interaction between the OpenSees environment and the deep reinforcement learning environment is realized. By adjusting the parameters in the reward function, the control preference of the DRL algorithm for different metrics can be effectively modified. Additionally, an intelligent structural vibration control platform based on DRL has been developed to simplify the design process of DRL algorithms. Case studies conducted on the platform demonstrate that DRL can effectively suppress structural responses in both single-layer and multi-layer complex structures. Meanwhile, comparisons with PID and LQR algorithms that are based on linear analysis design, reveal the stability advantages of DRL in handling structural dynamic responses characterized by high nonlinearity, time delay, and large actuator output intervals.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146656","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

Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function 基于weierstrass mandelbrot函数的多变量台风风速分形数值模拟

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-11-22 DOI: 10.1016/j.iintel.2024.100135

Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni

{"title":"Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function","authors":"Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni","doi":"10.1016/j.iintel.2024.100135","DOIUrl":"10.1016/j.iintel.2024.100135","url":null,"abstract":"<div><div>This paper proposes a fractal-based technique for simulating multivariate nonstationary wind fields by the stochastic Weierstrass Mandelbrot function. Upon conducting a systematic fractal analysis, it was found that the structure function method is more suitable and reliable than the box counting method, variation method, and R/S analysis method for estimating the fractal dimension of the stochastic wind speed series. Wind field measurement at the meteorological gradient tower with a height of 356 m in Shenzhen was conducted during Typhoon Mandelbrot (1983). Significant non-stationary properties and fractal dimensions of typhoon wind speed data at various heights were analyzed and used to demonstrate the effectiveness of the proposed multivariate typhoon wind speed simulation method. The multivariate wind speed components simulated by the proposed fractal-based method are in good agreement with the measured records in terms of the fractal dimension, standard deviation, probability density function, wind spectrum and cross-correlation coefficient.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146658","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

A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method 基于多尺度自适应降噪变压器-BiGRU模型和误差修正方法的地铁站点PM2.5浓度预测新方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-11-17 DOI: 10.1016/j.iintel.2024.100128

Dingyu Chen, Hui Liu

{"title":"A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method","authors":"Dingyu Chen, Hui Liu","doi":"10.1016/j.iintel.2024.100128","DOIUrl":"10.1016/j.iintel.2024.100128","url":null,"abstract":"<div><div>PM2.5 is a significant contributor to air pollution, with a notable impact on human health. Subway stations, with their high pedestrian traffic, present a particular challenge in this regard. By monitoring PM2.5 levels, subway managers can take prompt action, such as optimizing the operation of air purification equipment in stations, to enhance air quality within stations and thereby enhance the passenger experience. This paper proposes an enhanced Transformer-BiGRU prediction model, which incorporates a MSHAM(Multiscale Hybrid Attention Mechanism)comprising a multi-scale convolutional attention mechanism and a VMD decomposition self-attention mechanism. Additionally, a ANR(Adaptive Noise Reduction) module has been integrated into the model to facilitate noise reduction. Finally, the prediction is performed by BiGRU. The resulting error sequence is predicted by BiGRU and the predicted sequence is corrected. In this paper, a dataset of pollutants from Seoul subway stations in South Korea is used to compare with the base model. The model presented in this paper achieves the highest accuracy.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130220","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

Review on optimization strategies of probabilistic diagnostic imaging methods 概率成像诊断方法的优化策略综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-22 DOI: 10.1016/j.iintel.2024.100127

Ning Li , Anningjing Li , Jiangfeng Sun

{"title":"Review on optimization strategies of probabilistic diagnostic imaging methods","authors":"Ning Li , Anningjing Li , Jiangfeng Sun","doi":"10.1016/j.iintel.2024.100127","DOIUrl":"10.1016/j.iintel.2024.100127","url":null,"abstract":"<div><div>With the continuous development of intelligent infrastructure, structural health monitoring (SHM) and non-destructive testing (NDT) have become major research focuses. Ultrasonic-guided wave imaging technology not only integrates the global impact of damage on structures but also provides intuitive localization and severity characterization of the damage. Probabilistic diagnostic imaging (PDI) methods, which do not require direct interpretation of guided wave signals and can achieve high-quality imaging with sparse arrays, have garnered increasing attention. This paper introduces the principles, general processes, and technical advantages of PDI methods. Based on the process of the PDI, existing optimization strategies are categorized into two types: internal process optimizations, which include sensor layout, damage indices optimization, construction of the distribution weight function, and data fusion; and external process optimizations, which include spurious image suppression, on-site environment detection, and integration of methodologies, each analyzed in detail. With the affirmation of the value of these strategies, this paper also highlights the current issues within these methods and explores potential future developments by integrating emerging technologies such as intelligent sensing, big data, and artificial intelligence. These insights provide valuable reference suggestions for the continued optimization of these methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586147","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

An integrated management system (IMS) approach to sustainable construction development and management 可持续建筑开发和管理的综合管理系统（IMS）方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-18 DOI: 10.1016/j.iintel.2024.100126

Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou

{"title":"An integrated management system (IMS) approach to sustainable construction development and management","authors":"Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou","doi":"10.1016/j.iintel.2024.100126","DOIUrl":"10.1016/j.iintel.2024.100126","url":null,"abstract":"<div><div>Construction is significantly contributing to the severe environmental crisis it is facing. The sector consumes over 3 billion tons of raw materials annually, and its activities account for 40% of global CO<sub>2</sub> emissions. Traditional integrated strategies toward fragmented sustainability cannot offer total optimization. In this respect, the present research presents an integrated management system (IMS) containing a composite of metrics for sustainable construction management (SCM). This research was specifically geared to test the relationship between the elements of IMS and SCM from the perspective of the construction industry. A quantitative survey tested through 119 professionals was used for data collection. It is established through structural equation modeling (SEM) that the internal consistency of Cronbach’s Alpha 0.72–0.95 and construct validity was strong. The Fornell-Larcker criterion was realized to affirm good discriminant validity. Crucial results identified the presence of significant impacts for quality management (QM) (β = 0.643, <em>p</em> < 0.001), risk management (RM) (β = 0.53, <em>p</em> < 0.001), and safety management (SM) (β = 0.439, <em>p</em> < 0.001). Therefore, this study further enhances the scalability of IMS so that it is practically applied to improve project quality and safety, along with risk management. Future research could also focus on studying the context of the integration of IMS with SCM and continue to work using objective performance measures to validate these findings.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555002","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

Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point 结构消防工程的基于可靠性的安全格式。基于最可能失效点的推导

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-18 DOI: 10.1016/j.iintel.2024.100125

Ruben Van Coile, Balša Jovanović, Florian Put

{"title":"Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point","authors":"Ruben Van Coile, Balša Jovanović, Florian Put","doi":"10.1016/j.iintel.2024.100125","DOIUrl":"10.1016/j.iintel.2024.100125","url":null,"abstract":"<div><div>Designing structures for burnout resistance ensures stability during evacuation and search and rescue operations, limits collateral damage, and enhances post-fire repairability. This represents a significant shift from traditional prescriptive designs that do not evaluate performance under realistic fire conditions. However, given the variability in fire exposure and structural response, it is unclear which input values should be used to ensure a high level of reliability for burnout calculations. This paper introduces a safety format for burnout resistance compatible with the Eurocode and its reliability principles. The format allows users to specify desired reliability levels and prescribes equations for determining design values for load effects and fire load density using predetermined sensitivity weights. A method for calculating default sensitivity weights is outlined, proposing tentative values: 0.65 for resistance effect, −0.40 for load effect, and −0.80 for fire load density, with a default coefficient of variation of 0.30 for resistance effect when case-specific information is lacking. The safety format's performance is verified through case studies of a concrete slab and a numerical evaluation of a steel column, showing satisfactory and conservatively assessed results. Inherent conservatism in the design format may, however, occasionally lead to the undue rejection of designs. Further investigations are necessary to confirm the safety format's conceptualization, default sensitivity weights, and the influence of the adopted compartment fire model.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130219","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