Abstract
There are two specific aims in this study; first is to develop and validate an automated crack detection technique for the fire damaged beam. Second is to investigate whether the detected crack information and thermal-structural behaviors can be numerically related. To fulfill the aims, fire tests and residual strength tests are conducted on RC beams having different fire exposure time periods and sustained load levels. To detect the automated cracks, surface images of the fire damaged beam surfaces are taken with digital cameras and an automatic crack detection method is developed using a convolutional neural network (CNN) which is a deep learning technique primarily used for analyzing intricate structures of high-dimensional data [such as high definition (HD) images and videos]. The quantity of cracks detected using the proposed CNN changes depending on the test variables, and the changing trends are similar to those of the crack lengths obtained from the optical observation. Additionally, it is found that the quantity of the automatically detected cracks is numerically related to the temperatures inside the beams as well as the stiffnesses obtained from the residual strength tests.
Highlights
Concrete is known as a thermal resistant material, concrete structures are damaged when exposed to fire
This study investigates the ratio of the number of pixels obtained from the convolutional neural network (CNN) model to the crack length obtained from the optical observation, in order to see if consistency of the ratios can be found
The following conclusions are drawn: 1. The crack information of the fire damaged concrete beams obtained from the proposed CNN model agrees well with the crack information obtained from the optical observation
Summary
Concrete is known as a thermal resistant material, concrete structures are damaged when exposed to fire. One of the common investigation methods is optical observation of crack and deformation from the fire damaged structures. It would be cost effective if such optical observation is done quantitatively without requiring expensive testing machines or man power. It would be very powerful if the crack information can be. In the studies by Short et al (2001) and Guise (1997), color image analysis is used to investigate changes in color for concrete subjected to elevated temperatures. There is a study about concrete specimens with pre-made cracks to investigate the effect of cracks on temperature distributions
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