Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/38491

TitleTunnel engineering – influence of the type and the quantity of measurements in the back analysis of geomechanical parameters
Author(s)Miranda, Tiago F. S.
Daniel Dias
Pinheiro, Marisa Mota
Eclaircy-Caudron, S.
Keywordstunnel
monitoring data
inverse analysis
optimisation algorithms
numerical modelling
optimisation algorithms and numerical modelling
Issue date2016
PublisherTaylor & Francis
JournalEuropean Journal of Environmental and Civil Engineering
Citation71. Miranda, T., Dias, D., Pinheiro, M., & Eclaircy-Caudron, S. (2016). Tunnel engineering - influence of the type and the quantity of measurements in the back analysis of geomechanical parameters. European Journal of Environmental and Civil Engineering, 20(1), 60-78. doi: 10.1080/19648189.2015.1013640
Abstract(s)The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.
TypeArticle
URIhttp://hdl.handle.net/1822/38491
DOI10.1080/19648189.2015.1013640
ISSN2116-7214
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ISISE - Artigos em Revistas Internacionais

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