Compressive Strength of Concrete Estimated by Artificial Neural Networks and a Non-destructive Testing of Ultrasound

2021 ◽  
pp. 57-70
Author(s):  
R. S. Cavalcanti ◽  
F. A. N. Silva ◽  
J. M. P. Q. Delgado ◽  
A. C. Azevedo
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Non Destructive

scholarly journals A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2598
Author(s):  
Romain Cormerais ◽  
Aroune Duclos ◽  
Guillaume Wasselynck ◽  
Gérard Berthiau ◽  
Roberto Longo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Complex Structure ◽  
Fusion Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Aluminum Block ◽  
Artificial Neural ◽  
Non Destructive

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

Detection of the Propagation of Defects in Pressurized Pipes Through the Acoustic Emission Technique Using Artificial Neural Networks

2006 ◽  
Author(s):  
Sergio Damasceno Soares ◽  
Romeu Ricardo da Silva
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Emission Testing ◽  
Acoustic Emission Testing ◽  
Artificial Neural ◽  
Acoustic Emission Test ◽  
Non Destructive

The acoustic emission test has distinguished relevance in non-destructive testing and, therefore, existing research abound at present aiming at the improvement of the reliability of their results. In this work, the methodologies and the results obtained in a study performed are presented to implement pattern classifiers by using artificial neural networks, aiming at the propagation of existing defects in pressurized pipes by means of Acoustic Emission testing (AE). Parameters that are characteristic of AE signals were used as input data for the classifiers. Several tests were performed and the classification performances were in the range of 92% for most of the instances analyzed. Studies of parameter relevance were also performed and showed that only a few of the parameters are actually important for the separation of classes of signals corresponding to No Propagation (NP) of defects and Propagation (P) of defects. The results obtained are pioneering in this type of research and encouraged the present publication.

The Approximate Location of Damage through the Analysis of Natural Frequencies with Artificial Neural Networks

1995 ◽  
Vol 209 (2) ◽  
pp. 117-123 ◽  
Author(s):  
P C Kaminski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Natural Frequencies ◽  
Structural Members ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Approximate Location ◽  
Artificial Neural ◽  
Non Destructive

An effective and reliable damage assessment methodology is a valuable tool for the timely determination of damage and the deterioration stage of structural members as well as for non-destructive testing (NDT). In this work artificial neural networks are used to identify the approximate location of damage through the analysis of changes in the natural frequencies. At first, a methodology for the use of artificial neural networks for this purpose is described. Different ways of pre-processing the data are discussed. The proposed approach is illustrated through the simulation of a free-free beam with a crack whose natural frequencies were obtained experimentally.

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO DETERMINE CONCRETE COMPRESSIVE STRENGTH BASED ON NON‐DESTRUCTIVE TESTS

2005 ◽  
Vol 11 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Jerzy Hoła ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Concrete Compressive Strength ◽  
Data Set ◽  
Strength Based ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Non Destructive

The paper deals with the neural identification of the compressive strength of concrete on the basis of non‐destructively determined parameters. Basic information on artificial neural networks and the types of artificial neural networks most suitable for the analysis of experimental results are given. A set of experimental data for the training and testing of neural networks is described. The data set covers a concrete compressive strength ranging from 24 to 105 MPa. The methodology of the neural identification of compressive strength is presented. Results of such identification are reported. The results show that artificial neural networks are highly suitable for assessing the compressive strength of concrete. The neural identification of the compressive strength of concrete has been verified in situ.

scholarly journals Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

scholarly journals Using Support Vector Machine to Improve Ultrasonic Pulse Velocity Test for Concrete

2018 ◽  
Vol 207 ◽  
pp. 01001
Author(s):  
Tu Quynh Loan Ngo ◽  
Yu-Ren Wang
Keyword(s):  
Compressive Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

In the construction industry, to evaluate the compressive strength of concrete, destructive and non-destructive testing methods are used. Non-destructive testing methods are preferable due to the fact that those methods do not destroy concrete samples. However, they usually give larger percentage of error than using destructive tests. Among the non-destructive testing methods, the ultrasonic pulse velocity test is the popular one because it is economic and very simple in operation. Using the ultrasonic pulse velocity test gives 20% MAPE more than using destructive tests. This paper aims to improve the ultrasonic pulse velocity test results in estimating the compressive strength of concrete using the help of artificial intelligent. To establish a better prediction model for the ultrasonic pulse velocity test, data collected from 312 cylinder of concrete samples are used to develop and validate the model. The research results provide valuable information when using the ultrasonic pulse velocity tests to the inputs data in addition with support vector machine by learning algorithms, and the actual compressive strengths are set as the target output data to train the model. The results show that both MAPEs for the linear and nonlinear regression models are 11.17% and 17.66% respectively. The MAPE for the support vector machine models is 11.02%. These research results can provide valuable information when using the ultrasonic pulse velocity test to estimate the compressive strength of concrete.

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