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Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm

机译：利用多策略人工鱼群算法优化支持向量回归的隧道工程力学参数反演

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摘要

Fast and efficient determination of the mechanical parameters of surrounding rock masses is vitally important to the calculation and evaluation of the stability of surrounding rock masses in tunnel engineering. In this paper, a displacement back-analysis (DBA) model is proposed to identify the mechanical parameters based on support vector regression (SVR) optimized by multi-strategy artificial fish swarm algorithm (MAFSA). The MAFSA adopts the differential evolution strategy, the particle swarm optimization strategy, the adaptive step size and phased vision strategy on the basis of artificial fish swarm algorithm (AFSA) to enhance the global search capability and improve convergence speed and optimization accuracy. Then, the kernel width and the penalty parameter of SVR are optimized by MAFSA, forming into MAFSA-SVR. Meanwhile, the training and testing samples for MAFSA-SVR are constructed by orthogonal design and forward calculation by FLAC3Dcode. Finally, the DBA model is established based on MAFSA-SVR and applied to the mechanical parameter inversion of surrounding rock masses in the Heshi tunnel with the following conclusion: the relative errors of all the mechanical parameters are less than 8% between the inversed values of the DBA model based on MAFSA-SVR and the actual values. The method proposed in this paper could provide an efficient tool for the mechanical parameter inversion of the tunnel surrounding rock masses.
机译：快速有效地确定围岩力学参数对于隧道工程中围岩稳定性的计算和评估至关重要。本文提出了一种基于多策略人工鱼群算法（MAFSA）优化的支持向量回归（SVR）的位移反分析（DBA）模型来识别力学参数。 MAFSA在人工鱼群算法（AFSA）的基础上，采用了差分进化策略，粒子群优化策略，自适应步长和阶段视觉策略，以增强全局搜索能力，提高收敛速度和优化精度。然后，通过MAFSA对SVR的内核宽度和惩罚参数进行优化，形成MAFSA-SVR。同时，通过正交设计构造了MAFSA-SVR的训练和测试样本，并通过FLAC3Dcode进行了正向计算。最后，基于MAFSA-SVR建立了DBA模型，并将其应用于河西隧道围岩力学参数反演，得出以下结论：反演值之间所有力学参数的相对误差均小于8％。基于MAFSA-SVR和实际值的DBA模型。本文提出的方法可以为隧道围岩力学参数反演提供有效的工具。

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