2016
DOI: 10.2172/1248797
|View full text |Cite
|
Sign up to set email alerts
|

Linear Array Ultrasonic Test Results from Alkali-Silica Reaction (ASR) Specimens

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
4
0

Year Published

2016
2016
2021
2021

Publication Types

Select...
3
3
2

Relationship

0
8

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 11 publications
1
4
0
Order By: Relevance
“…The same happens in Section 2 (Figure 5 b ) and in sections 1 and 4 (not illustrated). However, as seen in other studies, high HTI can indicate the presence of other types of distresses, such as freeze-thaw and ASR ( 30 , 31 ).…”
Section: Incipient Crack Detectionsupporting
confidence: 56%
See 1 more Smart Citation
“…The same happens in Section 2 (Figure 5 b ) and in sections 1 and 4 (not illustrated). However, as seen in other studies, high HTI can indicate the presence of other types of distresses, such as freeze-thaw and ASR ( 30 , 31 ).…”
Section: Incipient Crack Detectionsupporting
confidence: 56%
“…As such, a higher HTI value represents damaged concrete, whereas a low value indicates sounder concrete. The indicator was able to identify and quantify the level of freeze-thaw damage in specimens and of alkali–silica reaction (ASR) in similar concrete slabs ( 30 , 31 ).…”
Section: Data Collection and Processingmentioning
confidence: 99%
“…In the case of containment structures it is the thickness and high level of reinforcement which limit what can be achieved using commercially available equipment. Recent success in imaging thick concrete with the application of arrays and a Large Aperture Ultrasonic System (LAUS) [5,6] has demonstrated potential for the detection and characterization of defects within such concrete. With these successes, there is interest in moving the concepts forward to a sparse array and in providing modeling tools to design, evaluate and optimize inspections and then to estimate detection capability as a function of wall thickness, placement and level of reinforcement…”
Section: Figure 1 Containment Cross Section Schematic (Tendons Not Dmentioning
confidence: 99%
“…Therefore, we pursued the development of automated classification techniques for the non-destructive detection and localization of ASR in reconstructed ultrasound (US) tomography images. We investigated Frequency domain based methods for feature extraction using the Hilbert Transform Indicator (HTI) as a classification metric [35]. We also investigated automated classification based on neural network algorithm training that classified non-ASR and ASR samples based on features that were found to correlate with time and frequency domain features.…”
Section: Autom Ated Detection Of Asr In Concretementioning
confidence: 99%
“…All six regions of the specimen were included in the dataset. For ASR damage data, we used two specimens-the Ramses and Cleopatra specimens [35]. For the Ramses and Cleopatra specimens, we included the odd and even positions, respectively.…”
Section: Artificial Neural Network (Ann) Backgroundmentioning
confidence: 99%