基于改进粒子群算法的土参反演及基坑开挖变形预测

Soil Parameter Inversion and Foundation Pit Excavation Deformation Prediction Based on MLAPSO

摘要:

为克服传统智能优化算法精度低、效率慢、易陷入局部最优的问题，提出了一种基于多级学习的自适应粒子群优化算法（MLAPSO）。该算法引入佳点集策略及多重搜索机制，包括全局搜索、FDB机制及Levy飞行策略，CEC-2022基准函数测试表明，MLAPSO在搜索精度和稳定性方面显著优于传统优化算法。进一步结合基坑开挖的荷载—结构模型，提出了基于MLAPSO的土体参数反演及基坑分阶段开挖变形预测方法。应用某地铁站基坑变形监测数据进行验证，结果表明，该方法能准确反演土体参数，且利用反演参数预测的围护结构变形与实测变形吻合较好，验证了该方法的正确性。

Abstract:

To overcome the limitations of traditional intelligent optimization algorithms， such as low accuracy， slow convergence， and susceptibility to local optima， this paper proposes a multilevel learning adaptive particle swarm optimization （MLAPSO） algorithm. The algorithm incorporates a best-point set strategy and multiple search mechanisms， including global search， the FDB mechanism， and the Levy flight strategy. Tests on the CEC-2022 benchmark functions demonstrate that MLAPSO significantly outperforms traditional optimization algorithms in terms of search accuracy and stability. Furthermore， combined with the load-structure model of foundation pit excavation， it proposes a method for soil parameter inversion and staged deformation prediction of foundation pits based on MLAPSO. The method is validated using monitoring data from a metro station. Results show that the approach can accurately invert soil parameters， and the predicted deformation of the retaining structures based on these parameters aligns closely with measured deformations， confirming the effectiveness and reliability of the method.