Assessment of Cracking in Masonry Structures Based on the Breakage of Ordinary Silica-Core Silica-Clad Optical Fibers

Abstract

This paper presents a study on the suitability and accuracy of detecting structural cracks in brick masonry by exploiting the breakage of ordinary silica optical fibers bonded to its surface with an epoxy adhesive. The deformations and cracking of the masonry specimen, and the behavior of pilot optical signals transmitted through the fibers upon loading of the test specimen were observed. For the first time, reliable detection of structural cracks with a given minimum value was achieved, despite the random nature of the ultimate strength of the optical fibers. This was achieved using arrays of several optical fibers placed on the structural element. The detection of such cracks allows the degree of structural danger of buildings affected by earthquake or other destructive phenomena to be determined. The implementation of this technique is simple and cost effective. For this reason, it may have a broad application in permanent damage-detection systems in buildings in seismic zones. It may also find application in automatic systems for the detection of structural damage to the load-bearing elements of land vehicles, aircraft, and ships.

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

“…Of great importance for this study was the already available information on the research and application of various methods for detecting, measuring, and monitoring defects in brick and brick masonry structures [32][33][34][35][36][37]. A sensor technology based on smart bricks-piezoresistive brick sensors for a structural health monitoring system-has been used to monitor deformations and early detection of cracks in shear brickwork [32].…”

“…The most successful model of the resonant pulse method had a success rate of up to 85% [33]. Using a break of ordinary quartz optical fibers (6-12 pieces) attached to the surface of the masonry with epoxy adhesive to detect and monitor structural cracks in brickwork, with a detection probability tending to one, allowed us to determine the residual life of structural elements of a building exposed to natural and man-made anomalous phenomena [34]. Automatic brick segmentation and image crack quantification using machine learning was implemented using various AI methods.…”