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Charles R. Farrar

Other affiliations: Analysis Group
Bio: Charles R. Farrar is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Structural health monitoring & Sensor node. The author has an hindex of 70, co-authored 357 publications receiving 26338 citations. Previous affiliations of Charles R. Farrar include Analysis Group.


Papers
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01 Oct 1987
TL;DR: In this article, two scale-model structures representing an idealized auxiliary building were seismically tested and the results showed that the frequency response of these structures, when subjected to seismic design loads, will be below that predicted for the structure using conventional analysis methods.
Abstract: Two scale-model structures representing an idealized auxiliary building were seismically tested. The scales (1/42, 1/14) were chosen so that both structures were models of the prototype, and the 1/42-scale model was a 1/3-scale model of the 1/14-scale structure. Both models were constructed out of microconcrete. The 1/42-scale used wire mesh to simulate reinforcing, and the 1/14-scale used model deformed bars. The general result verified previous test experience in this program: the frequency response of these structures, when subjected to seismic design loads, will be below that predicted for the structure using conventional analysis methods. This implies the frequency content and magnitude of floor response spectra, in general, will not be as predicted from the structural analysis. The implication of this result for equipment and piping is under investigation. The recommendation of this program, based on testing thus far, is to verify the conclusions on larger real concrete structures of a geometry that will be agreed upon by the technical review group for this program. 5 refs., 17 figs., 7 tabs.

1 citations

Journal Article
TL;DR: In this article, the authors proposed a more reliable bridge safety and maintenance process by integrating both visual inspection and long-term structural health monitoring, and demonstrated the applicability of this approach on data from the Z24 bridge in Switzerland.
Abstract: Bridge Management System (BMS) is a decision-support tool developed to assist the authorities in determining how and when to make decisions regarding maintenance, repair, and rehabilitation of structures in a systematic way. However, despite the advances in BMS modeling, the condition assessment activities still rely heavily on visual inspections, which inherently produce widely variable results. On the other hand, the goal of Structural Health Monitoring is to improve the safety and reliability of aerospace, civil, and mechanical infrastructure by detecting damage before it reaches a critical state. To achieve this goal, technology is being developed to replace qualitative visual inspection and time-based maintenance procedures with more quantifiable and automated damage assessment processes. It is the authors’ belief that for the activities related to bridge safety and maintenance should be based on visual inspections along with results from long-term monitoring. Over the last decade, the authors have realized that research in both fields has been conducted separately. Therefore, in order to develop a more reliable bridge safety and maintenance process, this paper summarizes the foundation of an approach to integrate both fields. The applicability of this approach is then demonstrated on data from the Z24 Bridge in Switzerland.

1 citations

Proceedings ArticleDOI
16 Mar 2006
TL;DR: In this article, a small series of all-composite test pieces emulating wings from a lightweight UAV have been developed to support damage detection and structural health monitoring (SHM) research.
Abstract: Carbon-fiber-reinforced-polymer (CFRP) composites represent the future for advanced lightweight aerospace structures. However, reliable and cost-effective techniques for structural health monitoring (SHM) are needed. Modal and vibration-based analysis, when combined with validated finite element (FE) models, can provide a key tool for SHM. Finite element models, however, can easily give spurious and misleading results if not finely tuned and validated. These problems are amplified in complex structures with numerous joints and interfaces. A small series of all-composite test pieces emulating wings from a lightweight all-composite Unmanned Aerial Vehicle (UAV) have been developed to support damage detection and SHM research. Each wing comprises two CFRP prepreg and Nomex honeycomb co-cured skins and two CFRP prepreg spars bonded together in a secondary process using a structural adhesive to form the complete wings. The first of the set is fully healthy while the rest have damage in the form of disbonds built into the main spar-skin bondline. Detailed FE models were created of the four structural components and the assembled structure. Each wing component piece was subjected to modal characterization via vibration testing using a shaker and scanning laser Doppler vibrometer before assembly. These results were then used to correlate the FE model on a component-basis, through fitting and optimization of polynomial meta-models. Assembling and testing the full wing provided subsequent data that was used to validate the numerical model of the entire structure, assembled from the correlated component models. The correlation process led to the following average percent improvement between experimental and FE frequencies of the first 20 modes for each piece: top skin 10.98%, bottom skin 45.62%, main spar 25.56%, aft spar 10.79%. The assembled wing model with no further correlation showed an improvement of 32.60%.

1 citations

24 Jun 2007
TL;DR: Phillip Cornwell, the authors is a Professor of Mechanical Engineering at the Rose-Hulman Institute of Technology (RHT) in New York, who has received an SAE Ralph R. Teetor Educational Award in 1992 and the Dean's Outstanding Teacher award at RHT in 2000.
Abstract: Phillip Cornwell, Rose-Hulman Institute of Technology Phillip Cornwell is a Professor of Mechanical Engineering at Rose-Hulman Institute of Technology. He received his Ph.D. from Princeton University in 1989 and his present interests include structural dynamics, structural health monitoring, and undergraduate engineering education. Dr. Cornwell has received an SAE Ralph R. Teetor Educational Award in 1992, and the Dean’s Outstanding Teacher award at Rose-Hulman in 2000.

1 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

9,627 citations

Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

ReportDOI
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