Topic
Structural health monitoring
About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.
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TL;DR: The effects of time synchronization error and data loss are investigated, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications and Coordinated computing is examined as a way to manage large amounts of data.
Abstract: Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems Limited network resources (eg battery power, storage space, bandwidth, etc) make these issues quite challenging This paper first investigates the effects of time synchronization error and data loss, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications Coordinated computing is then examined as a way to manage large amounts of data
144 citations
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TL;DR: The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.
Abstract: In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.
144 citations
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TL;DR: In this paper, a completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures.
Abstract: Summary
A newly developed, completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures. To provide contactless and practical monitoring, the current vision-based displacement measurement methods are improved by eliminating the physical target attachment. This is achieved by means of utilizing imaging key-points as virtual targets. As a result, pixel-based displacements of a monitored structural location are determined by using an improved detection and match key-points algorithm, in which false matches are identified and discarded almost completely. To transform pixel-based displacements to engineering units, a practical camera calibration method is developed because calibration standard on a physical target no longer exists. Moreover, a framework for evaluating the accuracy of vision-based displacement measurements is established for the first time, which, in return, provides users with the most crucial information of a measurement. The proposed framework along with a conventional sensor network and a data acquisition system are applied and verified on a real-life stadium during football games for structural assessment. The results obtained by the new method are successfully validated with the data acquired from sensors such as linear variable differential transformers and accelerometers. Because the proposed method does not require any type of sensor and target attachment, common field works such as sensor installation, wiring, maintaining conventional data acquisition systems are not required. This advantage enables an inexpensive and practical way for structural assessment, especially for real-life structures. Copyright © 2016 John Wiley & Sons, Ltd.
144 citations
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TL;DR: The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHm has been provided, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
Abstract: Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
143 citations
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TL;DR: In this article, an attenuation-based diagnostic method was proposed to assess the fastener integrity by observing the attenuation patterns of the resultant sensor signals, which is based on the damping phenomena of ultrasonic waves across the bolted joints.
Abstract: A concept demonstrator of the structural health monitoring (SHM) system was developed to autonomously detect the degradation of the mechanical integrity of the standoff carbon–carbon (C–C) thermal protection system (TPS) panels. This system enables us to identify the location of the loosened bolts, as well as to predict the torque levels of those bolts accordingly. In the process of building the proposed SHM prototype, efforts have been focused primarily on developing a trustworthy diagnostic scheme and a responsive sensor suite. In part I of the study, an attenuation-based diagnostic method was proposed to assess the fastener integrity by observing the attenuation patterns of the resultant sensor signals. The attenuation-based method is based on the damping phenomena of ultrasonic waves across the bolted joints. The major advantage of the attenuation-based method over the conventional diagnostic methods is its local sensing capability of loosened brackets. The method can further discriminate the two major failure modes within a bracket: panel-joint loosening and bracket-joint loosening. The theoretical explanation of the attenuation-based method is performed using micro-contact theory and structural/internal damping principles, followed by parametric model studies and appropriate hypothesis testing.
143 citations