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ETD Abstract Container

Abstract Header

An effective data mining approach for structure damage indentification

Hong, Soonyoung
http://rave.ohiolink.edu/etdc/view?acc_num=osu1194903908

Abstract Details

2007, Doctor of Philosophy, Ohio State University, Aeronautical and Astronautical Engineering.
An efficient, neural network based, online nondestructive structural damage identification procedure is developed for determining the damage characteristics (the damage locations and the corresponding severity) from dynamic measurements in near real-time. The procedure utilizes unique data processing techniques to track the most useful modal information based on modal strain energy and to calculate the associated data based on principal component analysis for further processing in a neural network based identification scheme. With two unique features, this approach is significantly different from currently available damage identification procedures for real-time structural integrity monitoring/diagnostics. First, the most sensitive mode for the specific damage is selected in an automatic process which increases the accuracy of damage identification and decreases time spent on neural network training. Second, the approach creates unique data that extracts core characteristics from modal information for a number of different damage cases; and consequently, the accuracy of the damage identification improves significantly. This approach can be operated online providing real time structural damage identification. The method is tested for simulated damage cases, including situations of single and multiple damage in the closely-spaced frequencies of Kabe's model. The philosophy behind the proposed research is to provide a means to online and nondestructively predict the degradation of a structure's integrity (i.e. damage location and the corresponding severity, strength loss).
Mo-How Shen (Advisor)
Structure Health Monitoring; Vibration Based Damage Identification; Principal Component Analysis; Modal Strain Energy

Recommended Citations

Citations

  • Hong, S. (2007). An effective data mining approach for structure damage indentification [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1194903908

    APA Style (7th edition)

  • Hong, Soonyoung. An effective data mining approach for structure damage indentification. 2007. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1194903908.

    MLA Style (8th edition)

  • Hong, Soonyoung. "An effective data mining approach for structure damage indentification." Doctoral dissertation, Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1194903908

    Chicago Manual of Style (17th edition)

osu1194903908
972
© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.