Structural health monitoring of civil infrastructure
15 Feb 2007-Philosophical Transactions of the Royal Society A (The Royal Society)-Vol. 365, Iss: 1851, pp 589-622
TL;DR: The motivations for and recent history of SHM applications to various forms of civil infrastructure are described, the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’ are discussed.
Abstract: Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'.
Citations
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TL;DR: Technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner and the historical overview and summarizing the SPR paradigm are provided.
Abstract: This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.
2,152 citations
Cites background from "Structural health monitoring of civ..."
...In this theme issue, articles byBrownjohn (2007) andLynch (2007) discuss further the applications of SHM to civil engineering infrastructure. In summary, the review of the technical literature presented by Doebling et al. (1996) andSohn et al....
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...In this theme issue, articles byBrownjohn (2007) andLynch (2007) discuss further the applications of SHM to civil engineering infrastructure....
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...In this theme issue, articles byBrownjohn (2007) andLynch (2007) discuss further the applications of SHM to civil engineering infrastructure. In summary, the review of the technical literature presented by Doebling et al. (1996) andSohn et al. (2003) showsan increasingnumber of research studies related to damage identification....
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TL;DR: The importance of implementing long-term structural health monitoring systems for large-scale bridges, in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse, has been recognized by bridge administrative authorities.
Abstract: The significance of implementing long-term structural health monitoring systems for large-scale bridges, in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse, has been recognized by bridge administrative authorities. Developing a long-term monitoring system for a large-scale bridge—one that is really able to provide information for evaluating structural integrity, durability and reliability throughout the bridge life cycle and ensuring optimal maintenance planning and safe bridge operation—poses technological challenges at different levels, from the selection of proper sensors to the design of a structural health evaluation system. This paper explores recent technology developments in the field of structural health monitoring and their application to large-scale bridge projects. The need for technological fusion from different disciplines, and for a structural health evaluation paradigm that is really able to help prioritize bridge rehabilitation, maintenance and emergency repair, is highlighted.
879 citations
TL;DR: In this paper, the authors provide a state-of-the-art review of guided wave based structural health monitoring (SHM) and highlight the future directions and open areas of research in guided wave-based SHM.
Abstract: The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.
664 citations
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
Abstract: 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.
500 citations
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Abstract: Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
440 citations
References
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01 May 1996
TL;DR: A review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response is presented in this article, where the authors categorize the methods according to required measured data and analysis technique.
Abstract: This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.
2,916 citations
"Structural health monitoring of civ..." refers background in this paper
...Despite the body of research dedicated to vibration-based damage detection (VBDD; Doebling et al. 1996), success is limited to simulations, laboratory studies and well-controlled experiments, such as the Z24 bridge (Maeck et al. 2001), and its effectiveness still remains to be proven for…...
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...Despite the body of research dedicated to vibration-based damage detection (VBDD; Doebling et al. 1996), success is limited to simulations, laboratory studies and well-controlled experiments, such as the Z24 bridge (Maeck et al....
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TL;DR: In this article, a review of stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions is presented. But it is not shown that many of these methods have an output-only counterpart.
Abstract: This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods havean output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.
849 citations
TL;DR: In this article, the authors used the analysis of vibration measurements as a tool for health monitoring of bridges, and the problem of separating abnormal changes from normal changes in the dynamic behaviour was identified.
Abstract: When using the analysis of vibration measurements as a tool for health monitoring of bridges, the problem arises of separating abnormal changes from normal changes in the dynamic behaviour Normal changes are caused by varying environmental conditions such as humidity, wind and most important, temperature The temperature may have an impact on the boundary conditions and the material properties Abnormal changes on the other hand are caused by a loss of stiffness somewhere along the bridge It is clear that the normal changes should not raise an alarm in the monitoring system (ie a false positive), whereas the abnormal changes may be critical for the structure's safety In the frame of the European SIMCES-project, the Z24-Bridge in Switzerland was monitored during almost one year before it was artificially damaged Black-box models are determined from the healthy-bridge data These models describe the variations of eigenfrequencies as a function of temperature New data are compared with the models If an eigenfrequency exceeds certain confidence intervals of the model, there is probably another cause than the temperature that drives the eigenfrequency variations, for instance damage Copyright © 2001 John Wiley & Sons, Ltd
788 citations
"Structural health monitoring of civ..." refers background or methods in this paper
...Successful SHM procedures will incorporate the means to compensate for or filter out the environmental and noise effects (Peeters & De Roeck 2001b) or at least establish confidence levels for anomaly detection against noise....
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...…more elaborate modal survey and model updating procedures are not likely to be used in all upgrade exercises, simplified forms of testing including operational analysis (Peeters & De Roeck 2001a) that can show an improvement in fundamental frequency could be used to show improvements in stiffness....
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...At about the same time, a range of system identification techniques for ambient response were developed that were precusors of the modern discipline of ‘operational modal analysis’ (Peeters & De Roeck 2001a)....
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11 Jul 2011
TL;DR: Udd et al. as discussed by the authors described the emergence of fiber-optic sensor technology and its application in industrial applications, including light sources, sensors, and sensors based on the Sagnac Interferometer and passive ring resonance.
Abstract: Preface. About the Authors. Contributors. 1 The Emergence of Fiber Optic Sensor Technology (Eric Udd). 2 Optical Fibers (Daniel A. Nolan, Paul E. Blaszyk, and Eric Udd). 3 Light Sources (Eric Udd). 4 Optical Detectors (William B. Spillman, Jr.). 5 Optical Modulators for Fiber Optic Sensors (Leonard M. Johnson). 6 Intensity-Based and Fabry Perot Interferometer Sensors (Gordon L. Mitchell). 7 Multimode Grating Sensors (William B. Spillman, Jr.). 8 Multimode Polarization Sensors (William B. Spillman, Jr.). 9 Fiber Optic Sensors Based on the Sagnac Interferometer and Passive Ring Resonator (Eric Udd). 10 Fiber Optic Sensors Based on the Mach Zehnder and Michelson Interferometers (Anthony Dandridge). 11 Distributed and Multiplexed Fiber Optic Sensors (Alan D. Kersey). 12 Fiber Optic Magnetic Sensors (Frank Bucholtz). 13 Industrial Applications of Fiber Optic Sensors (John W. Berthold III). 14 Fiber Optic Smart Structures (Eric Udd). 15 Fiber Grating Sensors (Eric Udd). 16 Fiber Optic Biosensors (William B. Spillman, Jr.). Index.
777 citations
Book•
01 Apr 1988
TL;DR: In this paper, the authors provide a guide to the entire process of geotechnical instrumentation, showing where to monitor safety and performance, and what benefits can be achieved, and how construction specifications should be written.
Abstract: This textbook provides a guide to the entire process of geotechnical instrumentation, showing where to monitor safety and performance. It contains chapters on the crucial steps of planning monitoring programmes using geotechnical instrumentation, including what benefits can be achieved, and how construction specifications should be written. Monitoring methods are described and evaluated, and recommendations are given for selection of the best instruments for monitoring: groundwater pressure; deformation; total stress in soil; stress change in rock; temperature; and load and strain in structural members. Detailed practical guidelines are given on instrument calibration, installation and maintenance, and on the collection, processing and interpretation of data. Chapters are included that describe the role of geotechnical instrumentation during the constructon and operation phases of various civil engineering projects including: braced excavations; embankments on soft ground; excavated and natural slopes; underground excavations; driven piles and driven shafts.
582 citations
"Structural health monitoring of civ..." refers methods in this paper
...…established sensor technology for civil SHM is well described elsewhere (Ross & Matthews 1995a), for geotechnical applications by Hanna (1985) and Dunnicliff (1994) and novel SHM sensors are described elsewhere in this journal issue, so only two new developments that have potential for SHM of…...
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