Survey: State of the Art in NDE Data Fusion Techniques

Liu, Zheng; Forsyth, David; Komorowski, Jerzy P.; Hanasaki, K.; Kirubarajan, T.

doi:10.1109/tim.2007.908139

Cited by 102 publications

(91 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Maierhofer et al (Maierhofer, Zacher, Khol, & Wöstmann, 2008) used radar, ultrasonic, impact-echo and thermography data fusion for predicting deterioration of concrete vehicular bridges, it was established that the data fusion technique increases the reliability, and improved the accuracy of the quantitative results, exploiting the different and sometimes complementary physical effects provided by different NDTs. Lui et al (Liu, Forsyth, & Komorowski, 2007) provide a very wide state-of-the-art regarding NDTs data fusion techniques, they established that the usage of multiple NDTs methods is employed in order to increase the reliability and reduce the uncertainty of testing and evaluation. Nonetheless, there are cases in which the use of all available tests creates a model that only works in the samples used to instantiate it, i.e., the model does not generalize.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Static Modulus of Elasticity Using Four non Destructive Testing

Chávez-García

Alonso-Guzmán

Martínez-Molina

et al. 2014

Revista de la Construcción

View full text Add to dashboard Cite

The static modulus of elasticity (Es) is an important parameter in the analysis of hydraulic concrete structures, changes have been made to the regulation of construction; these changes require minimum values for the Es, so now, in addition to concrete compressive strength (f´c) also Es values should be ensured. A methodology to predict Es is proposed, specifically, the Es were modeled by testing: ultrasonic pulse velocity (UPV), electrical resistivity test (ERT), resonance frequency test (RFT), the Hammer Test Rebound (HTR) and f´c. In order to generate models multiple linear regression technique was used. Cylindrical specimens were prepared in two stages, in the first stage was simulated laboratory conditions in the second stage was simulated conditions of concrete made in situ. All cylinders were subjected to non-destructive and destructive tests at different ages. The research objective is to predict Es from the results of destructive tests (traditionally employed to obtain Es) and nondestructive testing. It was possible to obtain a model whose correlation coefficient indicates the good approximation in the generated predictions. ResumenEl módulo estático de elasticidad (Es) es un parámetro importante en el análisis de las estructuras de concreto hidráulico, se han realizado cambios en la regulación de la construcción; estos cambios requieren valores mínimos de los Es, por lo que ahora, además de la resistencia a la compresión del concreto (f'c) también deben garantizarse valores de Es. Se propone una metodología para predecir Es, específicamente, los Es fueron modelados con las pruebas: velocidad de pulso ultrasónico (UPV), prueba de resistividad eléctrica (ERT), prueba de frecuencia de resonancia (RFT), prueba de rebote de martillo (HTR) y f'c. Se utilizó la técnica de regresión lineal múltiple para generar los modelos. Se elaboraron especímenes cilíndricos en dos etapas, la primera simulando condiciones de laboratorio y en la segunda simulando condiciones de concreto hecho en campo. Todos los cilindros se sometieron a pruebas no destructivas y destructivas a diferentes edades. El objetivo de la investigación es predecir Es con los resultados obtenidos de pruebas destructivas (empleadas tradicionalmente para obtener Es) y pruebas no destructivas. Fue posible obtener un modelo cuyo coeficiente de correlación indica la buena aproximación en las predicciones generadas.Palabras clave: Concreto hidráulico, Modulo de Elasticidad Estático, Modulo de Elasticidad Dinámico, Prueba no Destructivas, Resistencia a la compresión.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of the Static Modulus of Elasticity Using Four non Destructive Testing

Chávez-García

Alonso-Guzmán

Martínez-Molina

et al. 2014

Revista de la Construcción

View full text Add to dashboard Cite

show abstract

“…Therefore, in order to fully conduct the process of nondestructive imaging of the structure state data fusion of multiple features extracted from both methods results were carried out. The multiple sources inspection make the evaluation system more robust for the unwanted disturbances and increase the overall performance of the accession process [9], [10]. The block diagram of the algorithm utilized in this paper is presented in Fig.…”

Section: Data Fusionmentioning

confidence: 99%

“…After the new components extraction multiresolution analysis (MRA) was carried out to obtain final data fusion results [10] In this paper the wavelet decomposition WT was utilized. WT is one of the most effective decompositions methods allowing to obtain a good resolution in both time and frequency domain.…”

Section: Data Fusionmentioning

confidence: 99%

Coupled Active Thermography and Terahertz Technique in Composite Materials Defects' Evaluation

Szymanik¹,

Łopato²,

Psuj³

2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

View full text Add to dashboard Cite

In this paper the technique of defects evaluation in composite materials based on a combination of active thermography method and terahertz technique is presented. The composite glass fiber sample with delaminations produced by load conditions was chosen for the tests. Active Infrared thermography with halogen lamps and terahertz technique are sensitive to changes in different physical parameters of the tested material. The combination of these techniques, performed by the data fusion of obtained measurements results allows to obtain the full information on the detected material's defect.

show abstract

“…The purpose of data fusion is to combine the capabilities of each sensor modality with historic data to provide more accurate and complete information [88], [60]. Three factors should be considered:…”

Section: Data Fusion and Machine Learningmentioning

confidence: 99%

Computational Intelligence for Urban Infrastructure Condition Assessment: Water Transmission and Distribution Systems

Liu

Kleiner

2014

IEEE Sensors J.

Self Cite

View full text Add to dashboard Cite

Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=4ff847c4-1867-4c6d-8c41-478336da86f8 http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=4ff847c4-1867-4c6d-8c41-478336da86f8 Abstract-Water transmission and distribution systems are critical urban infrastructure. The aging of water mains can lead to increased breakage rate, decreased hydraulic capacity, and deterioration of water quality. Condition assessment of water mains encompasses building computational model of failures, discerning distress indicators from inspection, rating health condition, and forecasting future failures. In this process, computational intelligence helps to achieve high-level awareness of system condition and facilitates the decision making in water main renewal and rehabilitation by using the combined information from field knowledge, historical records, inspection results, and sensory data. This paper reviews computational approaches to achieve condition assessment of water mains. Inspection and sensor technologies involved in the assessment process are also briefly discussed.

show abstract

Survey: State of the Art in NDE Data Fusion Techniques

Cited by 102 publications

References 46 publications

Prediction of the Static Modulus of Elasticity Using Four non Destructive Testing

Prediction of the Static Modulus of Elasticity Using Four non Destructive Testing

Coupled Active Thermography and Terahertz Technique in Composite Materials Defects' Evaluation

Computational Intelligence for Urban Infrastructure Condition Assessment: Water Transmission and Distribution Systems

Contact Info

Product

Resources

About