Utilization of artificial neural networks for detection and classification of damages in composite plate-like structures via ultrasonic guided waves

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RIAHI MOHAMMAD; AHMADI ALIREZA

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Journal: Journal of Science and Technology of Composites Year:2018 | Volume:5 | Issue:3 Page(s): 343-352

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Title

Utilization of artificial neural networks for detection and classification of damages in composite plate-like structures via ultrasonic guided waves

Author(s)

RIAHI MOHAMMAD | AHMADI ALIREZA | Issue Writer Certificate

Keywords

Artificial neural networksQ1

Structural health monitoringQ1

Ultrasonic guided wavesQ1

Composite platesQ2

Wavelet transformQ1

Abstract

Using methods based on artificial intelligence to reduce the role of human interpretations in data analysis and obtaining the favorable results, in line with increasing the speed, reducing the errors and adjustment of the costs in the nondestructive evaluation and Structural health monitoring is seriously concerned by researchers. In this study, the design and implementation of a Structural health monitoring system is performed by the intelligent signal processing of the ultrasonic waves in order to identify and classify the three common defects in the composite plate-like structures. By creating three types of damages including delamination, crack and hole in the multi-layer composite plate made of glass fiber reinforced polymer and dividing it into 4 different zones, 9 piezoelectric transducers with dual role of actuator and sensor are attached with their network arrangement and the propagated signals in the four mentioned zones on the 12 paths in three different directions including 240 signals were stored. In the next step, extraction of the features from the signals is conducted by the advanced signal processing techniques such as Wavelet transform and the findings have been used to train a neural network of advanced multilayer perceptron by back-propagation error method. The results show that the designed and trained neural network algorithm in this research is able to differentiate between the intact zone from the damaged ones. In addition, it has classified the types of current defects and damages in the structure with the acceptable efficiency (the average is about 80%), which can be generalized to the different conditions and configurations and unknown situations.

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APA: Copy

RIAHI, MOHAMMAD, & AHMADI, ALIREZA. (2018). Utilization of artificial neural networks for detection and classification of damages in composite plate-like structures via ultrasonic guided waves. JOURNAL OF SCIENCE AND TECHNOLOGY OF COMPOSITES (JSTC), 5(3 ), 343-352. SID. https://sid.ir/paper/251784/en

Vancouver: Copy

RIAHI MOHAMMAD, AHMADI ALIREZA. Utilization of artificial neural networks for detection and classification of damages in composite plate-like structures via ultrasonic guided waves. JOURNAL OF SCIENCE AND TECHNOLOGY OF COMPOSITES (JSTC)[Internet]. 2018;5(3 ):343-352. Available from: https://sid.ir/paper/251784/en

IEEE: Copy

MOHAMMAD RIAHI, and ALIREZA AHMADI, “Utilization of artificial neural networks for detection and classification of damages in composite plate-like structures via ultrasonic guided waves,” JOURNAL OF SCIENCE AND TECHNOLOGY OF COMPOSITES (JSTC), vol. 5, no. 3 , pp. 343–352, 2018, [Online]. Available: https://sid.ir/paper/251784/en

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