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Probabilistic analysis of the inverse analysis of an excavation problem
- Source :
- Computers and Geotechnics, Computers and Geotechnics, Elsevier, 2010, 37 (3), pp.391-398
- Publication Year :
- 2010
- Publisher :
- HAL CCSD, 2010.
-
Abstract
- cited By 14; This study presents the probabilistic analysis of the inverse analysis of an excavation problem. Two techniques are used during two successive stages. First, a genetic algorithm inverse analysis is conducted to identify soil parameters from in situ measurements (i.e. first stage of the construction project). For a given tolerable error between the measurement and the response of the numerical model the genetic algorithm is able to generate a statistical set of soil parameters, which may then serve as input data to a stochastic finite element method. The second analysis allows predicting a confidence interval for the final behaviour of the geotechnical structure (i.e. second stage of the project). The tools employed in this study have already been presented in previous papers, but the originality herein consists of coupling them. To illustrate this method, a synthetic excavation problem with a very simple geometry is used. © 2010 Elsevier Ltd. All rights reserved.
- Subjects :
- Probabilistic analysis
Engineering
Stochastic modelling
0211 other engineering and technologies
020101 civil engineering
02 engineering and technology
0201 civil engineering
Simple (abstract algebra)
Computational mechanics
Field test
genetic algorithm
Input datas
Stochastic finite element method
excavation
Stochastic systems
Sheet piles
Soil surveys
[SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph]
Finite element method
Computer Science Applications
inverse analysis
Soil parameters
Geotechnical structure
Mathematical optimization
Numerical models
probability
finite element method
[SPI.MECA] Engineering Sciences [physics]/Mechanics [physics.med-ph]
Geologic models
sheet piled wall
Set (abstract data type)
Simple geometries
Genetic algorithm
Probabilistic analysis of algorithms
In-situ measurement
021101 geological & geomatics engineering
stochasticity
business.industry
Confidence interval
Excavation
prediction
Genetic algorithms
Geotechnical Engineering and Engineering Geology
Construction industry
Construction projects
Stochastic models
Soils
business
numerical model
Subjects
Details
- Language :
- English
- ISSN :
- 0266352X and 18737633
- Database :
- OpenAIRE
- Journal :
- Computers and Geotechnics, Computers and Geotechnics, Elsevier, 2010, 37 (3), pp.391-398
- Accession number :
- edsair.doi.dedup.....9db6104a28ec0d5d00ebceae8cf58daa