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Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization

机译:基于帕累托多目标优化的动力压实水力耦合模拟参数估计

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摘要

This paper presented a parameter estimation method based on a coupled hydromechanical model of dynamic compaction and the Pareto multiobjective optimization technique. The hydromechanical model of dynamic compaction is established in the FEM program LS-DYNA. The multiobjective optimization algorithm, Nondominated Sorted Genetic Algorithm (NSGA-IIa), is integrated with the numerical model to identify soil parameters using multiple sources of field data. A field case study is used to demonstrate the capability of the proposed method. The observed pore water pressure and crater depth at early blow of dynamic compaction are simultaneously used to estimate the soil parameters. Robustness of the back estimated parameters is further illustrated by a forward prediction. Results show that the back-analyzed soil parameters can reasonably predict lateral displacements and give generally acceptable predictions of dynamic compaction for an adjacent location. In addition, for prediction of ground response of the dynamic compaction at continuous blows, the prediction based on the second blow is more accurate than the first blow due to the occurrence of the hardening and strengthening of soil during continuous compaction.
机译:本文提出了一种基于动态压实水力耦合模型和帕累托多目标优化技术的参数估计方法。在FEM程序LS-DYNA中建立了动态​​压实的流体力学模型。多目标优化算法非支配排序遗传算法(NSGA-IIa)与数值模型集成在一起,可以使用多个现场数据源来识别土壤参数。通过现场案例研究来证明所提出方法的能力。动态压实早期打击时观测到的孔隙水压力和火山口深度同时用于估算土壤参数。后向估计参数的鲁棒性由前向预测进一步说明。结果表明,经过反分析的土壤参数可以合理地预测侧向位移,并为邻近位置的动力压实提供普遍可接受的预测。另外,对于连续击打时的动态压实的地面响应的预测,由于在连续压实过程中发生了土壤的硬化和强化,因此基于第二击的预测比第一击更准确。

著录项

  • 来源
    《Shock and vibration》 |2015年第4期|127878.1-127878.15|共15页
  • 作者单位

    Shanghai Jiao Tong Univ, Dept Civil Engn, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Civil Engn, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Civil Engn, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Civil Engn, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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