Detection and Quantification of Cracking in Concrete Aggregate through Virtual Data Fusion of X-Ray Computed Tomography Images
Clicks: 221
ID: 270481
2020
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.2
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
0.6
/100
2 views
2 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
In this work, which is part of a larger research program, a framework called “virtual data fusion” was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray computed tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in alkali-silica reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then “fused” in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (199 words).
Try re-searching for a better abstract.
| Reference Key |
oesch2020materialsdetection
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Tyler Oesch;Frank Weise;Giovanni Bruno;Oesch, Tyler;Weise, Frank;Bruno, Giovanni; |
| Journal | Materials (Basel, Switzerland) |
| Year | 2020 |
| DOI |
10.3390/ma13183921
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.