Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images
Construction and Building Materials, ISSN: 0950-0618, Vol: 324, Page: 126717
2022
- 6Citations
- 12Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S095006182200407X; http://dx.doi.org/10.1016/j.conbuildmat.2022.126717; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85124021687&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S095006182200407X; https://dx.doi.org/10.1016/j.conbuildmat.2022.126717
Elsevier BV
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