| 000 | 02641nam a2200289Ia 4500 | ||
|---|---|---|---|
| 003 | MX-MdCICY | ||
| 005 | 20250625162436.0 | ||
| 040 | _cCICY | ||
| 090 | _aB-19750 | ||
| 245 | 1 | 0 | _aThe use of intelligent computational tools for damage detection and identification with an emphasis on composites - A review |
| 490 | 0 | _vComposite Structures, 196, p.44-54, 2018 | |
| 520 | 3 | _aToday, the Structural Health Monitorin (SHM)methodology is the main way to deal with the detection and identification of damages in a range of engineering sectors, mainly in the military and civil aerospace industry. The need to monitor damages and failures in increasingly complex structures has led to the development of various detection techniques. Identification of damages through intelligent signal processing and optimization algorithms are particularly emphasized. The methods discussed here are mainly elaborated by the evaluation of modal data due to the great potential of their application. Moreover, the optimization of damage identification is approached through methods of optimal positioning of sensors for the acquisition of the data that must be evaluated so that conclusions can be made. This article discusses the use of computational and intelligent techniques for structural monitoring in the form of a review with emphasis on composite materials. Despite the excellent mechanical performance already known about composite materials, they have a weak point. While damages, in a metallic material are easily visible (in some cases), composite materials often have the superficial appearance as if in perfect condition, when, inside, there are serious damages. This paper can be seen as a guideline or a starting point for developing and improving SHM systems. The contents of this paper aim to help engineers and researchers find a starting point in developing a better solution to their specific structural monitoring problems, either by inverse methods, pattern recognition, and intelligent signal processing. | |
| 650 | 1 | 4 | _aDAMAGE DETECTION |
| 650 | 1 | 4 | _aSTRUCTURAL HEALTH MONITORING |
| 650 | 1 | 4 | _aOPTIMIZATION ALGORITHMS |
| 650 | 1 | 4 | _aARTIFICIAL NEURAL NETWORKS |
| 650 | 1 | 4 | _aMODAL DATA |
| 650 | 1 | 4 | _aINVERSE PROBLEM |
| 700 | 1 | 2 | _aGomes, G. F. |
| 700 | 1 | 2 | _aMendéz, Y. A. D. |
| 700 | 1 | 2 | _aAlexandrino, P. D. S. L. |
| 700 | 1 | 2 | _aDa Cunha Jr, S. S. |
| 700 | 1 | 2 | _aAncelotti Jr, A. C. |
| 856 | 4 | 0 |
_uhttps://drive.google.com/file/d/1Fj2s6dM4Y7fzwZFOAzDLmExSPAlzCigh/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
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