Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging and Linear Discriminant Analysis

Abstract

The deterioration of concrete structures due to rebar corrosion is a key issue affecting the safety and service life of civil infrastructure. Reinforced concrete (RC) structures in coastal areas are subjected to harsh environmental conditions that cause rebar corrosion. From the perspective of safety, repair, and structural rehabilitation, it is essential to ascertain the level of corrosion severity and associated damage in RC structures through non-destructive evaluation (NDE) techniques. In this study, the potential of pattern recognition techniques for ascertaining the severity damage at various stages of rebar corrosion in concrete samples was explored. A contact ultrasonic compressional wave transducer pair with 250 kHz centre frequency was used as source and reflected signals from the rebar were acquired using a tied-together scanning approach. To expedite the corrosion process in the laboratory, accelerated corrosion of the embedded rebar was employed. The synthetic aperture focusing technique (SAFT) was applied to reconstruct the image of the concrete subsurface from the acquired B-scans. Two approaches, i.e., the Mahalanobis distance (MD) and linear discriminant analysis (LDA), were adopted; both methods correctly classified the level of corrosion severity and damage to the concrete. The developed pattern recognition techniques can, therefore, be potential tools for generating important information towards economical and timely repair of damaged concrete structures affected by rebar corrosion.

Revolution of Shakespearean Plays' Genre Research: Exploring New Avenues through Machine Learning and Shapley Value Analysis

“…LDA is a classical machine learning method that aims to achieve efficient sample classification by finding an optimal projection direction, mapping data from a high-dimensional space to a lowerdimensional space [25] . The optimal projection direction in LDA is defined as the vector that maximizes the distance between classes while minimizing the within-class variance, as illustrated in  In practical applications, this method often faces challenges related to the difficulty of estimating within-class scatter matrices and their potential non-invertibility issues [26] .…”

An entropy-based probabilistic model for acoustic emission RA-value-average ‎frequency data

“…Capturing the initiation and evolution of cracks in the structures is of utmost importance to safeguard the structures. Among the nondestructive evaluation (NDE) methods (e.g., vision-based [1][2][3], ultrasound [4,5]), acoustic emission (AE) has shown great success [6][7][8][9][10][11][12]. In using AE data, researchers have used RA values versus AF to distinguish the shift in the cracking mechanisms within a structure [13][14][15][16][17][18][19].…”