Computer Aided Slope Stability Analysis Using Optimization And Parallel Computing Techniques
Slope stability analysis is commonly performed using limit equilibrium methods (LEM). In LEM, factor of safety (FS) is calculated for different trial slip surfaces and the one with the minimum FS is reported as the critical slip surface. Since locating the critical slip surface is believed to...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/45139/1/Mohammad%20Tabarroki24.pdf |
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| 總結: | Slope stability analysis is commonly performed using limit equilibrium methods
(LEM). In LEM, factor of safety (FS) is calculated for different trial slip surfaces and
the one with the minimum FS is reported as the critical slip surface. Since locating
the critical slip surface is believed to be an NP-hard (non-deterministic polynomialtime)
problem, heuristic global optimization techniques are employed. Although
these techniques have usually produced good results, “No Free Lunch” (NFL)
theorems seem to have made the problem of locating the critical slip surface an
endless research. According to the NFL theorems, no heuristic optimization
technique can perform well for all problems. On the other hand, there may exist other
slip surfaces that are as important as the critical slip surface in practical analyses. A
slip surface is important, if it is located far away from the critical slip surface, but
gives FS close to the minimum FS or the consequences of failure along the slip
surface is serious. Therefore, there is a need to constantly implement and test
different optimization techniques. However, implementation of optimization
techniques in slope stability analysis is often not straightforward because it requires
internal links to LEM. Firstly, the present study resolves this issue by developing a
decoupled algorithm that allows for easy implementation of optimization techniques.
Then, to demonstrate the simplicity of this algorithm and to promote the latest
research on slope stability, three state-of-the-art optimization techniques are
implemented, and their effectiveness and efficiency in detecting single/multiple
global and local minima is investigated on a series of test problems. |
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