Analysis of Structural Integrity of a Building Using an Artificial Neural Network ARTMAP-Fuzzy-Wavelet

This paper presents an ARTMAP-Fuzzy-Wavelet artificial neural network to perform the analysis of the structural integrity of a building. The combination of Fuzzy ARTMAP neural network, wavelets transform to generate a tool that performs the identification and characterization of structural failure. This method is applied as a support tool for professionals in the inspection of mechanical and building structures to identify and characterize flaws in order to carry out preventive maintenance to ensure the integrity of the structure and decision making. In order to validate the methodology perform mathematical modeling of a building of two walk, and from this model were simulated different situations (base-line condition and improper conditions), yielding a database of signals that serve as input ARTMAP-Fuzzy-Wavelet neural network. The results obtained by ARTMAP-Fuzzy-Wavelet shown efficiency and robustness.

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Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.838-841.3287 ◽

2013 ◽

Vol 838-841 ◽

pp. 3287-3290 ◽

Cited By ~ 3

Author(s):

Adriano dos Santos e Souza ◽

Fábio Roberto Chavarette ◽

Fernando Parra dos Anjos Lima ◽

Mara Lúcia Martins Lopes ◽

Simone Silva Frutuoso de Souza

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Structural Integrity ◽

Base Line ◽

Structural Failure ◽

Resonance Theory ◽

Adaptive Resonance ◽

Artificial Neural ◽

Identification And Characterization

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

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Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1025-1026.1107 ◽

2014 ◽

Vol 1025-1026 ◽

pp. 1107-1112 ◽

Cited By ~ 7

Author(s):

Fernando Parra dos Anjos Lima ◽

Simone Silva Frutuoso de Souza ◽

Fábio Roberto Chavarette ◽

Mara Lúcia Martins Lopes ◽

Antonio Eduardo Turra ◽

...

Keyword(s):

Neural Network ◽

Decision Making ◽

Artificial Neural Network ◽

Numerical Model ◽

Fault Identification ◽

Structural Failure ◽

Aircraft Structures ◽

Artificial Neural ◽

Aluminum Beam

This paper presents a methodology to perform the monitoring and identification of flaws in aircraft structures using an ARTMAP-Fuzzy-Wavelet artificial neural network. This technique is used in the detection and characterization of structural failure. The main application of this method is to assist in the inspection of aircraft structures in order to identify and characterize failures as well as decision-making, in order to avoid accidents or air crashes. In order to evaluate this method, the modeling and simulation of signals from a numerical model of an aluminum beam was performed. The results obtained by the method are satisfactory compared to literature.

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