000			04498nam a22005055i 4500
001			978-1-84996-323-7
003			DE-He213
005			20140220084516.0
007			cr nn 008mamaa
008			100603s2010 xxk| s |||| 0|eng d
020			_a9781849963237 _9978-1-84996-323-7
024	7		_a10.1007/978-1-84996-323-7 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aMarwala, Tshilidzi. _eauthor.
245	1	0	_aFinite-element-model Updating Using Computional Intelligence Techniques _h[electronic resource] : _bApplications to Structural Dynamics / _cby Tshilidzi Marwala.
264		1	_aLondon : _bSpringer London, _c2010.
300			_aXVI, 247p. 18 illus., 2 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_ato Finite-element-model Updating -- Finite-element-model Updating Using Nelder–Mead Simplex and Newton Broyden–Fletcher–Goldfarb–Shanno Methods -- Finite-element-model Updating Using Genetic Algorithm -- Finite-element-model Updating Using Particle-swarm Optimization -- Finite-element-model Updating Using Simulated Annealing -- Finite-element-model Updating Using the Response-surface Method -- Finite-element-model Updating Using a Hybrid Optimization Method -- Finite-element-model Updating Using a Multi-criteria Method -- Finite-element-model Updating Using Artificial Neural Networks -- Finite-element-model Updating Using a Bayesian Approach -- Finite-element-model Updating Applied in Damage Detection -- Conclusions and Emerging State-of-the-art.
520			_aFinite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: • multi-layer perceptron neural networks for real-time FEM updating; • particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; • simulated annealing to put the methodologies into a sound statistical basis; and • response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity. Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating. Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.
650		0	_aEngineering.
650		0	_aComputer simulation.
650		0	_aComputer science.
650		0	_aMechanical engineering.
650		0	_aCivil engineering.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aStructural Mechanics.
650	2	4	_aComputational Science and Engineering.
650	2	4	_aSimulation and Modeling.
650	2	4	_aMathematical Modeling and Industrial Mathematics.
650	2	4	_aCivil Engineering.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781849963220
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84996-323-7
912			_aZDB-2-ENG
999			_c111016 _d111016