# A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique
## 1 Introduction  
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 climacter phase of fractures that may lead to the catastrophic loss of capital and human lives. Usually, 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 easily be solved by using direct or nondirect monitoring methods that use measurements from various sensors [1, 2]. There are two types of direct methods. One is contact, and the other is the noncontact method, depending on if there is any direct attachment of the sensors to the test specimen. Photographic techniques [3, 4, 5], ultrasonic propagation imaging [6], etc. are some of the examples of direct noncontact techniques, and techniques such as 2D sensing sheets or fiber optics fall under the category of direct contact techniques. Nondirect methods include nondestructive techniques like acoustic emission (AE) to monitor the condition of theconcrete structures [7]. Among all the nondestructive techniques, AE is quite effective and popular for its ability to detect cracks in composite materials, like concrete [8; 9; 10; 11; 12; 13; 14; 15]. When fractures take place in concrete beams, materials associated with the fracture emit energy and produce propagating waves, which can be identified using the mounted transducers placed over the surface area of the concrete beam [1; 3; 6; 8; 16; 17; 18]. Using the recorded AE signals, we can determine the present condition of the concrete beam. AE has drawn a great deal of attention in recent times as a promising technique for monitoring concrete structures. The detection of cracks and the assessment of the damage and location in concrete structures are operations that are performed by analyzing the AE signals. Using AE techniques, it is possible to listen to sounds caused because of the plastic deformation and micro-cracking in the materials. AE signals are sensitive to crack propagation and microscopic processes [19] and can help us to determine the stress waves produced by structural anomalies. AE has its unique ability to determine processes like crack propagation and debonding in materials related to the performance assessment of the structures. Additionally, AE signals acquired under heavy loads from the concrete beams can be used for the effective characterization and identification of cracks. There are two kinds of AE signals depending on the waveforms shapes [3]. We intend to use burst-type AE signals that are associated with brittle materials and can be used to detect and characterize cracks in them. As a result, there are many works in the literature [8; 9; 10; 11; 12; 13; 14; 15; 17; 18; 19; 20; 21; 22; 23; 24; 25] adopting the AE technique for evaluating the degradation of concrete beams. These works provide efficient and robust methods for fault diagnosis in reinforced concrete beams, but the following issues should also be addressed carefully:  
* Previously conducted researches have used different AE features for the detection and classification of the fractures in concrete beams. It is quite hard to sort out which features have a higher sensitivity to crack growth. Certain features may have sensitivity to the incipient cracks that occur in the time history, whereas some may show sensitivity to crack severity (fractures) during the final failure period. Apart from the classification and detection of the cracks, it is also of paramount importance to assess the degradation in concrete structures over their full lifetime so that drastic and catastrophic failures can be avoided. Existing works have not focused on this perspective of crack evaluation in concrete structures. * Machine learning-based classification algorithms in many cases would require failure data for training in order to build a condition monitoring system for concrete structures.