NLP Optimization Model as a Failure Mechanism for Geosynthetic Reinforced Slopes Subjected to Pore-Water Pressure
Publication: International Journal of Geomechanics
Volume 16, Issue 5
Abstract
The majority of slope failures are triggered by excessive rainfall and the consequent increase in pore-water pressure within the slope. This paper presents the results of a computer code that quantifies earth pressure coefficients. This code is based on limit-equilibrium analyses and is used for the internal design of geosynthetic reinforced soil structures and to identify the critical failure mechanism. The critical failure mechanism is the largest value of out-of-balance force. For this purpose, the nonlinear programing (NLP) approach was used, and a NLP optimization model, TMAX, was developed. The model was used for failure mechanisms, assuming that the failure surfaces were bilinear. The influence of pore-water pressure on the potential failure surface was analyzed. The model was developed under basic principles. Optimally, the system is best suited for structures with varying geometries, different backfill unit weights, varying types of soil shear resistance, and different pore-water pressures. A numerical example was used to demonstrate the effect of pore-water pressure on the required strength of reinforcement and on the efficiency of the introduced optimization approach.
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Published In
International Journal of Geomechanics
Volume 16 • Issue 5 • October 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Dec 2, 2014
Accepted: Aug 17, 2015
Published online: Jan 5, 2016
Discussion open until: Jun 5, 2016
Published in print: Oct 1, 2016
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