000			02106 a2200325 4500
001			1138111929
005			20250317100417.0
008			250312042017xx eng
020			_a9781138111929
037			_bTaylor & Francis _cGBP 49.99 _fBB
040			_a01
041			_aeng
072	7		_aTQ _2thema
072	7		_aTN _2thema
072	7		_aKNA _2thema
072	7		_aTQ _2bic
072	7		_aTN _2bic
072	7		_aKNAT _2bic
072	7		_aTEC063000 _2bisac
072	7		_aTEC005000 _2bisac
072	7		_aTEC009020 _2bisac
072	7		_a624.1710151962 _2bisac
100	1		_aChan Ghee Koh
245	1	0	_aStructural Identification and Damage Detection using Genetic Algorithms _bStructures and Infrastructures Book Series, Vol. 6
250			_a1
260			_bCRC Press _c20170616
300			_a140 p
520			_bRapid advances in computational methods and computer capabilities have led to a new generation of structural identification strategies. Robust and efficient methods have successfully been developed on the basis of genetic algorithms (GA). This volume presents the development of a novel GA-based identification strategy that contains several advantageous features compared to previous methods. Focusing on structural identification problems with limited and noise contaminated measurements; it provides insight into the effects of various identification parameters on the identification accuracy for systems with known mass. It then proposes a generalization for systems with unknown mass, stiffness and damping properties. The GA identification strategy is subsequently extended for structural damage detection. The findings of the output-only strategy and substructural identification represent a great leap forward from the practical point of view. This book is intended for researchers, engineers and graduate students in structural and mechanical engineering, particularly for those interested in model calibration, parameter estimation and damage detection of structural and mechanical systems using the state-of-the-art GA methodology.
700	1		_aMichael J. Perry _4A01
999			_c3030 _d3030