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您的检索式：关键字=structural learning
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| 1 | Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring显示文摘Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges. | Billie F. Spencer Jr. Vedhus Hoskere Yasutaka Narazaki | 2019 | Engineering2019，5，2： | 44 |
| 2 | The State of the Art of Data Science and Engineering in Structural Health Monitoring显示文摘Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion. | Yuequan Bao Zhicheng Chen Shiyin Wei Yang Xu Zhiyi Tang Hui Li | 2019 | Engineering2019，5，2： | 47 |
| 3 | A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis显示文摘In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure.We have shown that the developed machine learning algorithm can accurately and(practically)uniquely identify both prior static as well as impact loading conditions in an inverse manner,based on the residual plastic strain and plastic deformation as forensic signatures.The paper presents the detailed machine learning algorithm,data acquisition and learning processes,and validation/verification examples.This development may have significant impacts on forensic material analysis and structure failure analysis,and it provides a powerful tool for material and structure forensic diagnosis,determination,and identification of damage loading conditions in accidental failure events,such as car crashes and infrastructure or building structure collapses. | Shaofei Ren Guorong Chen Tiange Li Qijun Chen Shaofan Li | 2018 | Computer Modeling in Engineering & Sciences2018，，12： | 3 |
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