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

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.