<  Back to the Polytechnique Montréal portal

Performance-driven measurement system design for structural identification

James-A. Goulet and Ian F. C. Smith

Article (2013)

Accepted Version
Terms of Use: All rights reserved.
Download (1MB)
Cite this document: Goulet, J.-A. & Smith, I. F. C. (2013). Performance-driven measurement system design for structural identification. Journal of Computing in Civil Engineering, 27(4), p. 427-436. doi:10.1061/(asce)cp.1943-5487.0000250
Show abstract Hide abstract


Much progress has been achieved in the field of structural identification due to a better understanding of uncertainties, improvement in sensor technologies and cost reductions. However, data interpretation remains a bottleneck. Too often, too much data is acquired, thus hindering interpretation. In this paper, a methodology is described that explicitly indicates when instrumentation can decreases the ability to interpret data. The approach includes uncertainties along with dependencies that may affect model predictions. Two performance indices are used to optimize measurement system designs: monitoring costs and expected identification performance. A case-study shows that the approach is able to justify a reduction in monitoring costs of 50% compared with an initial measurement configuration.

Uncontrolled Keywords

Computer-aided design, Measurement System, Sensor placement, Uncertainties, dependencies, Expected Identifiability, System Identification, Monitoring

Open Access document in PolyPublie
Subjects: 1000 Génie civil > 1000 Génie civil
Department: Département des génies civil, géologique et des mines
Research Center: Non applicable
Funders: Swiss National Science Foundation
Grant number: 200020-117670/1
Date Deposited: 15 Jan 2018 15:21
Last Modified: 24 Oct 2018 16:12
PolyPublie URL: https://publications.polymtl.ca/2888/
Document issued by the official publisher
Journal Title: Journal of Computing in Civil Engineering (vol. 27, no. 4)
Publisher: ASCE
Official URL: https://doi.org/10.1061/(asce)cp.1943-5487.0000250


Total downloads

Downloads per month in the last year

Origin of downloads


Repository Staff Only