Passive monitoring of nonlinear relaxation of cracked polymer concrete samples using acoustic emission

Abstract

This work presents an acoustic emission (AE) monitoring of slow dynamics in micro-cracked polymer concrete (PC) samples. In order to obtain calibrated damage states, AE was first used to characterize the micro-damage mechanisms in real time when PC samples are submitted to three-point bending tests. Then, an unsupervised classification of AE data based on the Principle Component Analysis and the k-means clustering was applied to classify AE data. The AE monitoring of the nonlinear relaxation of PC samples revealed the existence of a silence period followed by AE hits belonging to two different damage classes. A similarity appeared between the properties of the detected AE hits obtained during the nonlinear relaxation and the quasi-static tests. Finally, this work shows that the dynamics of both mechanisms during the nonlinear relaxation are clearly different.

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

“…Carpinteri et al [8] presented a study on numerical modeling accompanied by in-situ monitoring for concrete beams. Yu et al [10] monitored slow dynamics of microcracks in polymer concrete (PC) beams using AE. The AE data was clustered using k-means clustering algorithm to classify damage states and characterize microdamage mechanisms.…”

“…NDT can reveal the internal status of the materials and provide real-time information on the condition of the structure. Of all these methods, recently, AE has been proven to be very effective for composite materials like concrete [10][11][12][13][14][15][16][17]. During the fracture process in concrete beams, materials release energy and generate mechanical waves that propagate through the solid.…”

“…During the fracture process in concrete beams, materials release energy and generate mechanical waves that propagate through the solid. These mechanical waves can be detected by mounting AE transducers on surface of the concrete specimen [ 1 , 3 , 4 , 7 , 8 , 9 , 10 , 13 ] and consequently, the present condition of the concrete structures can be determined. Since pores and interfaces create heterogeneity, it is a complex task to assess the integrity of the concrete structures.…”

“…The burst type AE signals are acquired from the brittle materials and they are associated with crack propagation characteristically. Consequently, many research works [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] have adopted AE technique for evaluating degradation in concrete structures. These research articles provided effective approaches for crack/fracture diagnosis in concrete beams, but they failed to address certain key issues that are described as follows: The previous researches exploited numerous features such as rise time, decay time, amplitude, energy, ringing counts, RA and AF, etc.…”

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

“…If any crack occurs in a concrete beam, materials associated with the crack releases energy. This results in wave propagation that can be identified using mounted AE sensors set on the surface of the concrete beam [2], [9]- [14]. Recorded AE signals are used to determine the latest condition of the beam.…”