Determining the Optimal Placement of Sensors on a Concrete Arch Dam Using a Quantum Genetic Algorithm. (1st February 2016)
- Record Type:
- Journal Article
- Title:
- Determining the Optimal Placement of Sensors on a Concrete Arch Dam Using a Quantum Genetic Algorithm. (1st February 2016)
- Main Title:
- Determining the Optimal Placement of Sensors on a Concrete Arch Dam Using a Quantum Genetic Algorithm
- Authors:
- Zhu, Kai
Gu, Chongshi
Qiu, Jianchun
Liu, Wanxin
Fang, Chunhui
Li, Bo - Other Names:
- Kalantar-Zadeh Kourosh Academic Editor.
- Abstract:
- Abstract : Structural modal identification has become increasingly important in health monitoring, fault diagnosis, vibration control, and dynamic analysis of engineering structures in recent years. Based on an analysis of traditional optimization algorithms, this paper proposes a novel sensor optimization criterion that combines the effective independence (EFI) method with the modal strain energy (MSE) method. Considering the complex structure and enormous degrees of freedom (DOFs) of modern concrete arch dam, a quantum genetic algorithm (QGA) is used to optimize the corresponding sensor network on the upstream surface of a dam. Finally, this study uses a specific concrete arch dam as an example and determines the optimal sensor placement using the proposed method. By comparing the results with the traditional optimization methods, the proposed method is shown to maximize the spatial intersection angle among the modal vectors of sensor network and can effectively resist ambient perturbations, which will make the identified modal parameters more precise.
- Is Part Of:
- Journal of sensors. Volume 2016(2016)
- Journal:
- Journal of sensors
- Issue:
- Volume 2016(2016)
- Issue Display:
- Volume 2016, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 2016
- Issue:
- 2016
- Issue Sort Value:
- 2016-2016-2016-0000
- Page Start:
- Page End:
- Publication Date:
- 2016-02-01
- Subjects:
- Detectors -- Periodicals
681.205 - Journal URLs:
- https://www.hindawi.com/journals/js/ ↗
- DOI:
- 10.1155/2016/2567305 ↗
- Languages:
- English
- ISSNs:
- 1687-725X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10500.xml