dc.creator | Papadimitriou, C. | en |
dc.creator | Ntotsios, E. | en |
dc.date.accessioned | 2015-11-23T10:42:56Z | |
dc.date.available | 2015-11-23T10:42:56Z | |
dc.date.issued | 2008 | |
dc.identifier.isbn | 9783908158134 | |
dc.identifier.uri | http://hdl.handle.net/11615/31684 | |
dc.description.abstract | This work outlines the optimization algorithms involved in integrating system analysis and measured data collected from a network of sensors. The integration is required for structural health monitoring problems arising in structural dynamics and related to (1) model parameter estimation used for finite element model updating, (2) model-based damage detection in structures and (3) optimal sensor location for parameter estimation and damage detection. These problems are formulated as single- and multi-objective optimization problems of continuous or discrete-valued variables. Gradient-based, evolutionary, hybrid and heuristic algorithms are presented that effectively address issues related to the estimation of multiple local/global solutions and computational complexity arising in single and multi-objective optimization involving continuous and discrete variables. © 2008 Trans Tech Publications, Switzerland. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-62449219412&partnerID=40&md5=b5db6f2edfaee15f8c91206984ae99ea | |
dc.subject | Bayesian inference | en |
dc.subject | Damage detection | en |
dc.subject | Information entropy | en |
dc.subject | Optimal sensor location | en |
dc.subject | Structural dynamics | en |
dc.subject | Structural identification | en |
dc.subject | Discrete variables | en |
dc.subject | Finite-element model updating | en |
dc.subject | Gradient-based | en |
dc.subject | Integrating systems | en |
dc.subject | Measured datum | en |
dc.subject | Model parameter estimations | en |
dc.subject | Model-based | en |
dc.subject | Multi-objective optimization problems | en |
dc.subject | Multi-objective optimizations | en |
dc.subject | Network of sensors | en |
dc.subject | Optimization algorithms | en |
dc.subject | Structural healths | en |
dc.subject | System integrations | en |
dc.subject | Bayesian networks | en |
dc.subject | Computational complexity | en |
dc.subject | Dynamics | en |
dc.subject | Heuristic algorithms | en |
dc.subject | Inference engines | en |
dc.subject | Integrated control | en |
dc.subject | Integration | en |
dc.subject | Metal analysis | en |
dc.subject | Model structures | en |
dc.subject | Multiobjective optimization | en |
dc.subject | Parameter estimation | en |
dc.subject | Sensor networks | en |
dc.subject | Sensors | en |
dc.subject | Structural health monitoring | en |
dc.subject | Shape optimization | en |
dc.title | Optimization algorithms for system integration | en |
dc.type | conferenceItem | en |