Abstract
Two numerical procedures are described that quantitatively identify a set of constitutive parameters that best represents observed ground movement data associated with deep excavations in urban environments. This inverse problem is solved by minimizing an objective (or error) function of the weighted least-squares type that contains the difference between observed and calculated ground displacements. The problem is solved with two different minimization algorithms, one based on a gradient method and the other on a genetic algorithm. The objective function is shown to be smooth with a unique solution. Both methods are applied to lateral movements from synthetic and real excavations to illustrate various aspects of the implementation of the methods. The advantages and disadvantages of each method applied to excavation problems are discussed.
Original language | English (US) |
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Pages (from-to) | 331-345 |
Number of pages | 15 |
Journal | Computers and Geotechnics |
Volume | 35 |
Issue number | 3 |
DOIs | |
State | Published - May 2008 |
Funding
Financial support for this work was provided by National Science Foundation grant CMS-0219123 and the Infrastructure Technology Institute (ITI) of Northwestern University. The support of Dr. Richard Fragaszy, program director at NSF, and Mr. David Schulz, ITI’s director, is greatly appreciated. The writers would like to thank Y. Malécot, M. Boulon and E. Flavigny from the “Sols, Solides, Structures, Risques” laboratory, France, for their contributions in developing the GA optimization method.
Keywords
- Excavation
- Genetic algorithm
- Gradient method
- Inverse problem
- Objective function
- Parameter identification
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Computer Science Applications