Skip navigation

Optimization of condensed stiffness matrices for structural health monitoring

Optimization of condensed stiffness matrices for structural health monitoring

Tee, Kong Fah ORCID: 0000-0003-3202-873X (2019) Optimization of condensed stiffness matrices for structural health monitoring. Optimization of design for better structural capacity. IGI Global. ISBN 978-1522570592 (doi:https://doi.org/10.4018/978-1-5225-7059-2.ch006)

Full text not available from this repository. (Request a copy)

Abstract

This chapter aims to develop a system identification methodology for determining structural parameters of linear dynamic systems, taking into consideration practical constraints such as insufficient sensors. Based on numerical analysis of measured responses (output) due to known excitations (input), structural parameters such as stiffness values are identified. If the values at the damaged state are compared with the identified values at the undamaged state, damage detection and quantification can be carried out. To retrieve second-order parameters from the identified state space model, various methodologies developed thus far impose different restrictions on the number of sensors and actuators employed. The restrictions are relaxed in this study by a proposed method called the condensed model identification and recovery (CMIR) method. To estimate individual stiffness coefficient from the condensed stiffness matrices, the genetic algorithms approach is presented to accomplish the required optimization problem.

Item Type: Book Section
Uncontrolled Keywords: Structural health monitoring, Stiffness, System identification, Condensed model, Sensor, Damage detection, Optimization
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 28 Mar 2019 16:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/23140

Actions (login required)

View Item View Item