Fracture characterization of composite slabs with different connections based on acoustic emission parameters

Abstract

Summary Damage fracture characteristics of composite slabs with different connection configurations were analyzed using acoustic emission (AE) with cast‐in‐place (CIP) reinforced concrete (RC) slabs as contrast. AE characteristic parameters were obtained during static experiments and compared with each other. The bearing capacity was directly proportional to cumulative AE energy, rise time, and duration time, of which the correlation coefficient of rise time was the highest and reached 99.99%. The whole fracture process could be divided into three stages. Compared to CIP slabs, the rates of energy release in the former two stages were slower, which caused the worse ductility. Meanwhile, many other differences among the tested slabs were captured in aspects of energy release, crack development, and severity. Finally, the quantitative analysis of damage classifications was performed under various load levels. The experimental results were beneficial to advance the application of AE in fields of damage analysis and health monitoring for composite slabs.

Fractal characteristics and damage evaluation of corroded beams under four-point bending tests based on acoustic emission techniques

“…It uses AE sensors to passively capture elastic wave signals generated by material damage or deformed [9,[13][14][15]. AE technique has the advantages of high sensitivity [16] and the ability to detect damage in real-time [17], making it an effective tool for monitoring the evolution damage of RC structures. Therefore, many researchers have carried out laboratory experiments from different aspects to investigate the AE characteristics of corroded RC members.…”

Corrosion evaluation of prestressed high‐strength steel wires with impressed current cathodic protection based on acoustic emission technique

“…In Equations 3 and 4, the determination of the two canstants, J and K, is depending on the type of material, which means the values of J and K can be different for various materials. In the field of health monitoring of concrete structures based on AE technique, numerous researchers have conducted AE intensity analysis to assess the damage degree of concrete structures, and the determination of J and K for AE intensity analysis in concrete can be described as follows 49,50 : K is not applicable if N ≤ 50; K=N-30 if 50 < N ≤ 200; K = 0.85 N if 201 < N ≤ 500 and K=N-75 if N > 500. J is not applicable if N ≤ 50 and J = 50 if N > 50.…”

Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

“…In addition, the proposed IDCNN is trained on the parameters in error back propagation, and the gradient of the whole network is calculated by chain rule based on the gradients of the convolutional and pooling layers obtained. And the partial derivative of the loss function of IDCNN with respect to the convolution kernel and bias of the lth layer can be calculated by the following Equation (10).…”

“…As a dynamic nondestructive detection method, the dynamic stress and damage degree of material can be determined effectively according to AE parameters (e.g. AE amplitude, counts and energy accumulation) 8–10 . Specifically, an important characteristic of AE technology is that AE signals generally generate by damage defects, among which crack propagation is commonly regarded as signal source 11,12 .…”

“…AE amplitude, counts and energy accumulation). [8][9][10] Specifically, an important characteristic of AE technology is that AE signals generally generate by damage defects, among which crack propagation is commonly regarded as signal source. 11,12 In general, AE-based fault diagnosis approach mainly concentrates on two stages (i.e., AE feature extraction and source localization).…”