Title:
Using Machine Learning for Condition Assessment of Concrete Infrastructure
Author(s):
Leandro F.M. Sanchez and Marcelo Terra
Publication:
Concrete International
Volume:
41
Issue:
11
Appears on pages(s):
35-39
Keywords:
damage, image, structure, data
DOI:
10.14359/51721328
Date:
11/1/2019
Abstract:
The damage rating index (DRI) is among the most promising microscopy methods for condition assessment of concrete infrastructures. However, it requires experts to perform time-consuming petrographic microscopy of samples. The article discusses a study aimed at automating DRI assessments using machine learning (ML). It is expected that ML will reduce the subjectively yet increase the speed, reproducibility, accessibility, and accuracy of DRI determination.
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