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Practice of Optimisation Theory in Geotechnical Engineering pp 123–145Cite as

Parameter Identification for Granular Materials

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Abstract

The chapter proposes an efficient optimization procedure for identifying parameters of easily crushable sand, which is then applied to the pile driving simulation. The Nelder–Mead Simplex genetic algorithm (NMGA) is first proposed and a newly enhanced elastoplastic breakage model is adopted. Then, the performance of NMGA is validated by identifying parameters from synthetic tests, and further verified by triaxial tests on limestone grains, based on which the necessary number of objective tests is also suggested. The role of grain breakage in bearing capacity of driven pile is also discussed. All comparisons demonstrate that the proposed NMGA with breakage-model-based parameter identification procedure is efficient and effective for easily crushable sand.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China

    Zhen-Yu Yin & Yin-Fu Jin

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  1. Zhen-Yu Yin
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  2. Yin-Fu Jin
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© 2019 Springer Nature Singapore Pte Ltd. and Tongji University Press

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Yin, ZY., Jin, YF. (2019). Parameter Identification for Granular Materials. In: Practice of Optimisation Theory in Geotechnical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-3408-5_7

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  • DOI: https://doi.org/10.1007/978-981-13-3408-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3407-8

  • Online ISBN: 978-981-13-3408-5

  • eBook Packages: EngineeringEngineering (R0)

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