scholarly journals Effect of Freeze–Thaw Cycling on the Failure of Fibre-Cement Boards, Assessed Using Acoustic Emission Method and Artificial Neural Network

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2181 ◽  
Author(s):  
Tomasz Gorzelańczyk ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Freeze Thaw ◽  
Fibre Cement ◽  
Artificial Neural ◽  
Building Practice

This paper presents the results of investigations into the effect of freeze–thaw cycling on the failure of fibre-cement boards and on the changes taking place in their structure. Fibre-cement board specimens were subjected to one and ten freeze–thaw cycles and then investigated under three-point bending by means of the acoustic emission method. An artificial neural network was employed to analyse the results yielded by the acoustic emission method. The investigations conclusively proved that freeze–thaw cycling had an effect on the failure of fibre-cement boards, as indicated mainly by the fall in the number of acoustic emission (AE) events recognized as accompanying the breaking of fibres during the three-point bending of the specimens. SEM examinations were carried out to gain better insight into the changes taking place in the structure of the tested boards. Interesting results with significance for building practice were obtained.

scholarly journals Identification of the Degree of Degradation of Fibre-Cement Boards Exposed to Fire by Means of the Acoustic Emission Method and Artificial Neural Networks

Materials ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Krzysztof Schabowicz ◽  
Tomasz Gorzelańczyk ◽  
Mateusz Szymków
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Bending Strength ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Fibre Cement ◽  
Artificial Neural ◽  
Degree Of Degradation

This paper presents the results of research aimed at identifying the degree of degradation of fibre-cement boards exposed to fire. The fibre-cement board samples were initially exposed to fire at various durations in the range of 1–15 min. The samples were then subjected to three-point bending and were investigated using the acoustic emission method. Artificial neural networks (ANNs) were employed to analyse the results yielded by the acoustic emission method. Fire was found to have a degrading effect on the fibres contained in the boards. As the length of exposure to fire increased, the fibres underwent gradual degradation, which was reflected in a decrease in the number of acoustic emission (AE) events recognised by the artificial neural networks as accompanying the breaking of the fibres during the three-point bending of the sample. It was shown that it is not sufficient to determine the degree of degradation of fibre-cement boards solely on the basis of bending strength (MOR).

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Three-Point Bending Test Evaluation of Concrete Specimens by Non-Traditional Analysis of Acoustic Emission Method

2016 ◽  
Vol 837 ◽  
pp. 198-202
Author(s):  
Luboš Pazdera ◽  
Libor Topolář ◽  
Tomáš Vymazal ◽  
Petr Daněk ◽  
Jaroslav Smutny
Keyword(s):  
Acoustic Emission ◽  
Signal Analysis ◽  
Measurement Data ◽  
Bending Test ◽  
Test Evaluation ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Time Frequency ◽  
Emission Signal

The aim of the paper is focused on the analysis of the mechanical properties of the concrete specimens with plasticizer at three point bending test by the signal analysis of the acoustic emission signal. The evaluations were compared the measurement and the results obtained with theoretical presumptions. The Joint Time Frequency Analysis applied on measurement data and its evaluation is described. It is well known that the Acoustic Emission Method is a very sensitive method to determine active cracks into structure. However, evaluation of acoustic emission signals is very difficult. A non-traditional method was used to signal analysis of burst acoustic emission signals recorded during three point bending test.

The Assessment of High-Temperature Degraded Concrete Specimens of Different Mixtures by Acoustic Emission Method during Three-Point Bending Test and Pulse Velocity Method in Comparison with Destructive Tests

2017 ◽  
Vol 908 ◽  
pp. 88-93 ◽  
Author(s):  
Libor Topolář ◽  
Richard Dvořák ◽  
Luboš Pazdera
Keyword(s):  
Acoustic Emission ◽  
Building Materials ◽  
Pulse Velocity ◽  
Bending Test ◽  
Structural Components ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Live Loads ◽  
Non Destructive

One of the advantages of concrete over other building materials is its inherent fire-resistive properties. The concrete structural components still must be able to withstand dead and live loads without collapse even though the rise in temperature causes a decrease in the strength and modulus of elasticity for concrete and steel reinforcement. In addition, fully developed fires cause expansion of structural components and the resulting stresses and strains must be resisted. This paper reports the results of measurements by Acoustic Emission method during three-point bending test on concrete specimens. The Acoustic emission method is a non-destructive technique used widely for structural health monitoring purposes of structures. The sensors are mounted by beeswax on the surface of the material or structure to record the motion of the surface under the elastic excitation of the cracking sources. The concrete specimens were heated in a programmable laboratory furnace at a heating rate of 5 °C/min. The specimens were loaded at six temperatures, 200 °C, 400 °C, 600 °C, 800 °C, 1000 °C, and 1200 °C maintained for 60 minutes. The results are obtained in the laboratory.

scholarly journals Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Xiangxin Liu ◽  
Zhengzhao Liang ◽  
Yanbo Zhang ◽  
Xianzhen Wu ◽  
Zhiyi Liao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Good Method ◽  
Rock Types ◽  
Fracturing Process ◽  
Different Types ◽  
Artificial Neural ◽  
Ae Signals

Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.

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