Artificial Intelligence for Real-Time Crack Detection of Ultra-High-Performance Concrete
Jung Wang, Chao Liu, and Yail J. Kim
Appears on pages(s):
crack-induced damage; deep learning; fibers; ultra-high-performance concrete (UHPC)
This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.