Chẩn đoán vết nứt trong dầm nhôm sử dụng tín hiệu trở kháng kết hợp mạng nơ-ron nhân tạo

Chỉ số đề mục

67.11.29

Lĩnh vực nghiên cứu

Kỹ thuật xây dựng

Dạng tài liệu

Tác giả

Nguyễn Trung Hiếu, Lê Quang Huy, Hồ Đức Huy, Nguyễn Thị Thanh Trúc⁽¹⁾

Nhan đề

Chẩn đoán vết nứt trong dầm nhôm sử dụng tín hiệu trở kháng kết hợp mạng nơ-ron nhân tạo

Nhan đề tiếng anh

Crack detection in aluminum beams using impedance responses and artificial neural networks

Nguồn trích

Xây dựng

Năm xuất bản

2017

Số

11

Trang

125-129

ISSN

0866-8762

Từ khóa

Xây dựng, Dầm, Vết nứt, Mạng nơ-ron

Từ khóa tiếng anh

Tóm tắt

Bài báo giới thiệu phương pháp chẩn đoán vết nứt trong dầm nhôm sử dụng tín hiệu trở kháng kết hợp mạng nơ-ron nhân tạo

Tóm tắt tiếng anh

This paper introduces a method to detect cracks in aluminum beams by using a combination of electro-mechanical (EM) impedance responses and Artificial Neutral Networks (ANNs). Firstly, theories of EM impedance responses and impedance-based damage detection method are described. Secondly, the feasibility of numerical simulations for EM impedance responses are demonstrated by comparing to experimental results for an aluminum beam. Thirdly, the impedance-based damage detection method combined with ANN s are used to diagnose cracks in the beam. Analytical results reveal that the proposed method can accurately detect the occurrence and the extension of the cracks in the beam.

Kí hiệu kho

TTKHCNQG, CVv21

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Tài liệu tham khảo

[1] Sun, F. P., Chaudhry, Z., Liang, C., and Rogers, C. A. (1995), Truss structure integrity identification using PZT sensor-actuator,Journal of Intelligent Material Systems and Structures
[2] Park, S., Ahmad, S., Yun, C. S., and Roh, Y (2006), Multiple crack detection of concrete structures using impedance-based structural health monitoring techniques,Experimental Mechanics
[3] Park, G., Sohn, H., Farrar, C., and Inman, D. (2003), Overview of piezoelectric impedance-based health monitoring and path forward,The Shock and Vibration Digest
[4] Min, J., Park, S., Yun, C. B., Lee, C. G., and Lee, C. (2012), Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity,Engineering Structure
[5] Liu, X., and Jiang, Z. (2009), Design of a PZT patch for measuring longitudinal mode impedance in the assessment of truss structure damage,Smart Materials and Structures
[6] Liang, C., Sun, F. P., and Rogers, C. A. (1994), Coupled electro-mechanical analysis of adaptive material systems - Determination of the actuator power consumption and system energy transfer,Journal of Intelligent Material Systems and Structures
[7] Lee, J. J., Yun, C. B., Lee, J. W., and Jung, H. Y. (2005), Neural network-based damage detection for bridges considering errors in baseline finite element model,Journal of Sound and Vibration
[8] Giurgiutiu, V., and Zagrai, A. (2005), Damage detection in thin plates and aerospace structures with the electro-mechanical impedance method,Structural Health Monitoring
[9] Farrar, C. R. (2001), Historical overview of structural health monitoring,Lecture Notes on Structural Health Monitoring Using Statistical Pattern Recognition
[10] Bhalla, S. and Soh, C. K. (2003), Structural impedance-based damage diagnosis by piezo-transducers,Earthquake Eng Struct Dyn.