Skip to main content

Research Repository

Advanced Search

Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization

Zhang, Y.; Gallipoli, D.; Augarde, C.E.

Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization Thumbnail


Authors

Y. Zhang

D. Gallipoli



Abstract

Simulation-based optimization methods have been recently proposed for calibrating geotechnical models from laboratory and field tests. In these methods, geotechnical parameters are identified by matching model predictions to experimental data, i.e. by minimizing an objective function that measures the difference between the two. Expensive computational models, such as finite difference or finite element models are often required to simulate laboratory or field geotechnical tests. In such cases, simulation-based optimization might prove demanding since every evaluation of the objective function requires a new model simulation until the optimum set of parameter values is achieved. This paper introduces a novel simulation-based “hybrid moving boundary particle swarm optimization” (hmPSO) algorithm that enables calibration of geotechnical models from laboratory or field data. The hmPSO has proven effective in searching for model parameter values and, unlike other optimization methods, does not require information about the gradient of the objective function. Serial and parallel implementations of hmPSO have been validated in this work against a number of benchmarks, including numerical tests, and a challenging geotechnical problem consisting of the calibration of a water infiltration model for unsaturated soils. The latter application demonstrates the potential of hmPSO for interpreting laboratory and field tests as well as a tool for general back-analysis of geotechnical case studies.

Citation

Zhang, Y., Gallipoli, D., & Augarde, C. (2009). Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization. Computers and Geotechnics, 36(4), 604-615. https://doi.org/10.1016/j.compgeo.2008.09.005

Journal Article Type Article
Publication Date May 1, 2009
Deposit Date Mar 17, 2009
Publicly Available Date Apr 8, 2009
Journal Computers and Geotechnics
Print ISSN 0266-352X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 36
Issue 4
Pages 604-615
DOI https://doi.org/10.1016/j.compgeo.2008.09.005
Keywords Parameter identification, Unsaturated soils, Particle swarm optimization, Parallel computing.

Files






You might also like



Downloadable Citations