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Parameter identification for an embankment dam using noisy field data
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0001-6562-1738
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-1365-8552
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0003-1935-1743
(English)In: Proceedings of the Institution of Civil Engeneers: Geotechnical Engineering, ISSN 1353-2618, E-ISSN 1751-8563Article in journal (Refereed) Epub ahead of print
Abstract [en]

Field sampling for evaluation of the mechanical behaviour in embankment dams is not easily performed, since the performance and the safety of the structure may be unfavourably affected. A non-destructive method as inverse analysis is an alternative. In this study, inverse analysis has been utilised to identify values for soil parameters for an embankment dam. An objective function and a genetic search algorithm were combined with a finite element software to perform the analysis. Values of model parameters were calibrated until inclinometer deformations from monitoring and computations corresponded to each other. Errors in field measurements occur, related to e.g. measurement precision as well as handling and installation of the equipment. Search algorithms in mathematical optimisation might obtain numerical problems if they are used against data containing errors. The performance of the genetic algorithm was investigated for the studied dam, when identification was performed against inclinometer data containing known errors of different magnitudes. The results showed that the genetic algorithm can search for solutions without obtaining numerical problems, even though the field data is substantially perturbed. It was found that the genetic algorithm is able to find good solutions for data from field measurements including usual errors in practical dam applications.

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Geotechnical Engineering
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URN: urn:nbn:se:ltu:diva-77527DOI: 10.1680/jgeen.19.00163OAI: oai:DiVA.org:ltu-77527DiVA, id: diva2:1388710
Available from: 2020-01-27 Created: 2020-01-27 Last updated: 2020-02-04

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Toromanovic, JasminaKnutsson, SvenLaue, Jan
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