Campus Units
Civil, Construction and Environmental Engineering, Electrical and Computer Engineering, Center for Nondestructive Evaluation (CNDE)
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2017
Journal or Book Title
Structural Health Monitoring
DOI
10.1177/1475921717702537
Abstract
This work develops optimal sensor placement within a hybrid dense sensor network used in the construction of accurate strain maps for large-scale structural components. Realization of accurate strain maps is imperative for improved strain-based fault diagnosis and prognosis health management in large-scale structures. Here, an objective function specifically formulated to reduce type I and II errors and an adaptive mutation-based genetic algorithm for the placement of sensors within the hybrid dense sensor network are introduced. The objective function is based on the linear combination method and validates sensor placement while increasing information entropy. Optimal sensor placement is achieved through a genetic algorithm that leverages the concept that not all potential sensor locations contain the same level of information. The level of information in a potential sensor location is taught to subsequent generations through updating the algorithm’s gene pool. The objective function and genetic algorithm are experimentally validated for a cantilever plate under three loading cases. Results demonstrate the capability of the learning gene pool to effectively and repeatedly find a Pareto-optimal solution faster than its non-adaptive gene pool counterpart.
Research Focus Area
Structural Engineering
Copyright Owner
The Authors
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Downey, Austin; Hu, Chao; and Laflamme, Simon, "Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool" (2017). Civil, Construction and Environmental Engineering Publications. 149.
https://lib.dr.iastate.edu/ccee_pubs/149
Included in
Civil Engineering Commons, Structural Engineering Commons, VLSI and Circuits, Embedded and Hardware Systems Commons
Comments
This is a manuscript of an article published as Downey, Austin, Chao Hu, and Simon Laflamme. "Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool." Structural Health Monitoring (2017): 1475921717702537. DOI: 10.1177/1475921717702537. Posted with permission.