Abstract
This paper presents a concept for the application of particle swarm optimization in geotechnical engineering. For the calculation of deformations in soil or rock, numerical simulations based on continuum methods are widely used. The material behavior is modeled using constitutive relations that require sets of material parameters to be specified. We present an inverse parameter identification technique, based on statistical analyses and a particle swarm optimization algorithm, to be used in the calibration process of geomechanical models. Its application is demonstrated with typical examples from the fields of soil mechanics and engineering geology. The results for two different laboratory tests and a natural slope clearly show that particle swarms are an efficient and fast tool for finding improved parameter sets to represent the measured reference data.
Highlights
When compared to most other engineering tasks, geotechnical problems are often characterized by the following peculiarities
Continuum methods are used to calculate deformations in soil or rock, and the material behavior is simulated by means of constitutive models, which require a certain set of material parameters
This forward problem consists of a specified geometry with given initial and boundary conditions and a material model, which requires a set of material parameters to be determined
Summary
When compared to most other engineering tasks, geotechnical problems are often characterized by the following peculiarities. Due to the availability of sufficiently fast computer hardware, there has been a growing interest in the application of inverse parameter identification strategies and optimization algorithms to geotechnical modeling in order to make this procedure automated [1,2,3,4,5] and more traceable and objective This approach provides statistical information, which can be used to quantify the calibration quality of the developed geotechnical model. All cited references agree on the fact that back-calculation of model parameters by means of optimization routines is possible in the field of geotechnics, if an appropriate forward calculation depending on adequately realistic model assumptions is provided, for example, Calvello and Finno [1, 2] In this context, particle swarms represent a powerful tool for finding parameter sets that best represent the reference data, with acceptable calculation effort and time consumption
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