scholarly journals Non-destructive in situ Identification of the Moisture Content in Saline Brick Walls Using Artificial Neural Networks

2019 ◽  
Author(s):  
Anna Hoła ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Artificial Neural ◽  
Non Destructive

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 Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Wave resource characterization through in-situ measurement followed by artificial neural networks' modeling

2018 ◽  
Vol 115 ◽  
pp. 1055-1066 ◽  
Author(s):  
Antonio Santos Sánchez ◽  
Diego Arruda Rodrigues ◽  
Raony Maia Fontes ◽  
Márcio Fernandes Martins ◽  
Ricardo de Araújo Kalid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
In Situ Measurement ◽  
Artificial Neural
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