Abstract
This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.
Highlights
The wide and extensive use of concrete beams in modern-day engineering structures has made it necessary to assess the deterioration of such beams to avoid the climacteric phase of fractures that may lead to the catastrophic loss of capital and human lives
This section mainly evaluates the potential of the method described in Section 4.2 for the crack assessment indicator (CAI) development and classification
This paper proposes an approach that involves developing a robust CAI accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks of Reinforced concrete (RC) beams
Summary
The wide and extensive use of concrete beams in modern-day engineering structures has made it necessary to assess the deterioration of such beams to avoid the climacteric phase of fractures that may lead to the catastrophic loss of capital and human lives. Concrete structures are monitored through visual inspection, which is not always a feasible option due to the inaccessible locations of the cracks, especially in bigger structures. This problem can be solved by using direct or nondirect monitoring methods that use measurements from various sensors [1,2]. The other is the noncontact method, depending on if there is any direct attachment of the sensors to the test specimen. Nondirect methods include nondestructive techniques like acoustic emission (AE) to monitor the condition of the
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
