Title: Automated Detection of Surface Cracks and Numerical Correlation with Thermal-Structural Behaviors of Fire Damaged Concrete Beams
Author(s): Eunmi Ryu, Jewon Kang, Jieun Lee, Yeongsoo Shin and Heesun Kim
Appears on pages(s):
Keywords: fire, crack, RC beam, deep learning, convolutional neural network, edge detection
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 stiff-nesses obtained from the residual strength tests.