Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An improved substructural identification strategy for large structural systems
To identify physical parameters of a large structural system, the computational challenges in dealing with a large number of unknowns are formidable. A divide‐and‐conquer approach is often required to partition the structural system into many substructures, each with much lesser unknowns for more accurate and efficient identification. Furthermore, in view of the ill‐conditioned nature of inverse analysis, it is highly beneficial to adopt nongradient‐based search methods such as genetic algorithm (GA). To this end, this paper presents a GA‐based substructural identification strategy for large structural systems. As compared with some recent work on substructural identification, the proposed strategy presents two significant improvements: (i) the use of acceleration measurement to directly account for interaction between substructures without approximation of interface force; and (ii) the use of an improved identification method based on multi‐feature GA. In numerical simulations, the mass, damping, and stiffness parameters of a 100‐storey shear building, involving 202 unknowns, are identified with very good accuracy (mean error of less than 3%) based on incomplete acceleration measurements with 10% noise. In addition, an experimental study on a 10‐storey small‐scale steel frame further validates the superior performance of the proposed strategy over complete structural identification. Copyright © 2011 John Wiley & Sons, Ltd.
An improved substructural identification strategy for large structural systems
To identify physical parameters of a large structural system, the computational challenges in dealing with a large number of unknowns are formidable. A divide‐and‐conquer approach is often required to partition the structural system into many substructures, each with much lesser unknowns for more accurate and efficient identification. Furthermore, in view of the ill‐conditioned nature of inverse analysis, it is highly beneficial to adopt nongradient‐based search methods such as genetic algorithm (GA). To this end, this paper presents a GA‐based substructural identification strategy for large structural systems. As compared with some recent work on substructural identification, the proposed strategy presents two significant improvements: (i) the use of acceleration measurement to directly account for interaction between substructures without approximation of interface force; and (ii) the use of an improved identification method based on multi‐feature GA. In numerical simulations, the mass, damping, and stiffness parameters of a 100‐storey shear building, involving 202 unknowns, are identified with very good accuracy (mean error of less than 3%) based on incomplete acceleration measurements with 10% noise. In addition, an experimental study on a 10‐storey small‐scale steel frame further validates the superior performance of the proposed strategy over complete structural identification. Copyright © 2011 John Wiley & Sons, Ltd.
An improved substructural identification strategy for large structural systems
Trinh, Thanh N. (Autor:in) / Koh, Chan Ghee (Autor:in)
Structural Control and Health Monitoring ; 19 ; 686-700
01.12.2012
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Substructural and progressive structural identification methods
| Online Contents | 2003
Substructural identification using neural networks
| British Library Conference Proceedings | 1998
Concrete substructural waterproofing
| Engineering Index Backfile | 1912
Substructural Identification Method without Interface Measurement
| Online Contents | 2003