Journal of Infrastructure Intelligence and Resilience最新文献_第6页

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 : 2024-09-01 Epub 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":"2024-09-01","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

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 : 2024-09-01 Epub 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":"2024-09-01","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

Towards vision-based structural modal identification at low frame rate using blind source separation 利用盲源分离实现基于视觉的低帧频结构模态识别

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-09-01 Epub Date: 2024-02-21 DOI: 10.1016/j.iintel.2024.100085

Shivank Mittal , Ayan Sadhu

{"title":"Towards vision-based structural modal identification at low frame rate using blind source separation","authors":"Shivank Mittal , Ayan Sadhu","doi":"10.1016/j.iintel.2024.100085","DOIUrl":"10.1016/j.iintel.2024.100085","url":null,"abstract":"<div><p>With increasing availability of cost-effective and high-resolution cameras, their use as a non-contact sensing tool has rapidly progressed for structural health monitoring. The cameras offer unique capabilities to provide full-field measurement with high spatial density at low cost. However, extracting high-density temporal data is challenging, as a high-speed camera increases the monitoring cost with high-rate data processing. Recently, motion magnification (MM) has shown significant success in analyzing low-amplitude motion of structural systems. However, previous studies observed that MM methodology performs poorly at low frame rates for modal identifications. In this paper, the influence of low frame rate on phased-based motion magnification (PMM) has been investigated. A novel technique is proposed by combining PMM with zero mean-normalization cross-correlation tracker to determine vibrational responses, and then the spatial Wigner-Ville spectrum-based time-frequency blind source separation method is explored for modal identification using the extracted vibrational responses obtained from the video data. The experimental data of a lumped mass experimental model and a steel bridge is used to test the accuracy of the proposed method. The original and motion-magnified image response data is compared with accelerometer data for modal identification. The proposed method is able to extract the modal parameters with high accuracy for motion-magnified images, even for low frame rates.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000045/pdfft?md5=3856b76a2dacf10913cf7351487c87f3&pid=1-s2.0-S2772991524000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467224","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 : 2024-09-01 Epub 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":"2024-09-01","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

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 : 2024-09-01 Epub 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":"2024-09-01","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

Intrinsic self-sensing concrete to energize infrastructure intelligence and resilience: A review 内在自感应混凝土为基础设施的智能化和复原力注入活力：综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-06-01 Epub Date: 2024-03-08 DOI: 10.1016/j.iintel.2024.100094

Xinyue Wang , Siqi Ding , Yi-Qing Ni , Liqing Zhang , Sufen Dong , Baoguo Han

{"title":"Intrinsic self-sensing concrete to energize infrastructure intelligence and resilience: A review","authors":"Xinyue Wang , Siqi Ding , Yi-Qing Ni , Liqing Zhang , Sufen Dong , Baoguo Han","doi":"10.1016/j.iintel.2024.100094","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100094","url":null,"abstract":"<div><p>Under loading and environmental actions, infrastructures undergo continuous aging and deterioration of the constituent materials during their service lifespan. In-situ monitoring the aging and deterioration at material level of infrastructures can provide effective protection and maintenance prior to serious failure, thus enhancing their safety and lifespan as well as resilience. Therefore, self-sensing performance of materials is an important paradigm for updating infrastructures with intelligent digital insights. Concrete, the most widely used engineering material for infrastructure construction, inherently lacks self-sensing property. The incorporation of functional fillers can form a conductive sensory “neural” system inside concrete, thus empowering concrete with the capability to sense stress (or force), strain (or deformation), and damage (e.g., cracking, fatigue) in itself, and also improving (or maintaining) its mechanical properties and durability. The emergence of intrinsic self-sensing concrete has laid a material foundation for realizing in-situ monitoring, contributing to the development of intelligent and resilient infrastructures. This review concisely introduces the significant research progress of research on the composition and preparation, measurement and characterization, performance and control, mechanism and model, and application of intrinsic self-sensing concrete in civil and transportation infrastructures, as well as current challenges and roadmap for its future development.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000136/pdfft?md5=721fc57999551542e849532456d2c413&pid=1-s2.0-S2772991524000136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330698","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 Epub Date: 2024-06-14 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 : 2024-06-01 Epub 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":"2024-06-01","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

Structural damage quantification using long short-term memory (LSTM) auto-encoder and impulse response functions 利用长短期记忆（LSTM）自动编码器和脉冲响应函数量化结构损伤

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-06-01 Epub Date: 2024-02-23 DOI: 10.1016/j.iintel.2024.100086

Chencho , Jun Li , Hong Hao

{"title":"Structural damage quantification using long short-term memory (LSTM) auto-encoder and impulse response functions","authors":"Chencho , Jun Li , Hong Hao","doi":"10.1016/j.iintel.2024.100086","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100086","url":null,"abstract":"<div><p>This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected “principal components” to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000057/pdfft?md5=f3e5252bd85bf26600d9a4445daa485f&pid=1-s2.0-S2772991524000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140332871","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 classification for sensor anomalies with limited samples 在样本有限的情况下，对传感器异常情况进行少量分类

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-06-01 Epub Date: 2024-03-01 DOI: 10.1016/j.iintel.2024.100087

Yuxuan Zhang , Xiaoyou Wang , Yong Xia

{"title":"Few-shot classification for sensor anomalies with limited samples","authors":"Yuxuan Zhang , Xiaoyou Wang , Yong Xia","doi":"10.1016/j.iintel.2024.100087","DOIUrl":"10.1016/j.iintel.2024.100087","url":null,"abstract":"<div><p>Structural health monitoring (SHM) systems generate a large amount of sensing data. Data anomalies may occur due to sensor faults and extreme events. Sensor faults can result in low-fidelity measurement data, while data associated with extreme events are crucial for assessing the structural safety condition and should be given special attention. Accurate detection and classification of anomalies can improve the performance of SHM systems. However, most existing classification methods work well only when the number of a-single-class anomalies is sufficient. This study proposes an automatic few-shot classification method for sensor anomalies with limited labeled samples. The most discriminatory shapelet, a new representation of abnormal data, is learned from the standard normal class by maximizing the overall distance, which can locate the prominent abnormal features from 1-h acceleration data. The classification is then learned based on manual feature extraction and deep-learning-based feature extraction by measuring the similarity between the most discriminatory shapelets from the query and support sets. The proposed few-shot classification method is applied to datasets collected from two SHM systems of a long-span bridge and a campus footbridge. Results demonstrate that the proposed method can classify new anomalies with limited samples that differ from the defined anomalies.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000069/pdfft?md5=0510fe12562729a914ba390bb6ce1cb9&pid=1-s2.0-S2772991524000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089254","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