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005			20260818222854.0
008			150900s2000 th uzm rtt 00| a1eng d
035			_a.b11807696
099		9	_aAIT Thesis no.ST-00-05
100	0		_aWoottigrai Techaungkool
245	1	0	_aStructure damage detection using neural networks
260			_aBangkok : _bAsian Institute of Technology, _c2000
300			_a85 leaves
490	1		_aThesis ; _vno. ST-00-05
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Civil Engineering
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2000
520			_aDamage detection is a challenging problem that is under vigorous investigation by numerous research groups. When a structure suffers localized damage, its dynamic properties can change. Specially, damage can cause a stiffness reduction, with an inherent reduction in . natural frequencies, and increase in modal damping, and a change to the modal shapes. The development of experimental modal analysis techniques has facilitates the accurate measurement of modal parameters. Alongside this work, several methods have been developed to detect structural parameter change (structural damage) by using location-dependent changes in the modal data. This study focuses on the application of neural networks approach to the assessment of structural damage based on the modal test data. The procedure of using the neural networks to detect structural damage is outlined. A kind of training algorithms called as Back-propagation is used to recognize the modal data from numerical simulations, and the location and the extent of damage of a structure can be recognized by comparison of the outputs from the trained networks fed the modal test data obtained from the structure at the undamage and damage states. Some essential features of this algorithm that influences its searching efficiency are also discussed, especially, several practical concerns involving the structural damage detection are addressed, including the problem of incomplete mode shape measurements, the robustness of detection, and nonlinearity of system. The proposed approach is applied to measured mode data of a real 5 story steel frame. The comparison of numeric results with those from the traditional damage detection method indicates that the proposed method has the potential as a practical tool for a structure damage detection methodology.
650	1	0	_aStructural dynamics
650	1	0	_aNeural networks (Computer science)
700	1		_aZhu, Hongping, _eChairperson
700	0		_aWorsak Kanok-Nukulchai, _eExamination Committee
700	0		_aPennung Warnitchai, _eExamination Committee
700	1		_aBarry, William Joseph, _eExamination Committee
710	2		_aAIT Partial Scholarship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ST-00-05
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B06830
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909			_aBarcode : 30050120747224 _bCREATED : 2014-05-30 _cRECORD # : i12804952 _dLPATRON : 1028145 _eLCHKIN : 2018-03-09 _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 1 _jTOT RENEW : 0
909			_aBarcode : 30050160102058 _bCREATED : 2016-04-21 _cRECORD # : i12943769 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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