Existing component separation methods fail to consider the complex nonlinear relationship between dam effect quantities and environmental variables. In this study, a novel nonlinear component separation method for the effect quantities is proposed by combining kernel partial least squares (KPLS) and pseudosamples. By this method, a nonlinear monitoring model is established based on KPLS, and the complicated nonlinear relationship between the effect quantities and environmental variables can be determined accurately through the model. Furthermore, special pseudosamples are constructed to separate independent components and coupling influence components of environmental factors from the KPLS model. These methods have been applied into a super-high arch dam, and the separated displacement components conform to the general deformation law. The presented results indicate that it is more reliable than traditional multiple linear regression models.
The permeability coefficient of a concrete slab rockfill dam (CFRD) was calculated in this paper on the basis of the equivalent quasi-continuum model for the percolation of crack-intensive face. This calculation helped simplify the establishment of a finite element model and improve the efficiency of calculating the seepage of dams. Moreover, an inversion algorithm based on particle swarm optimization and support vector machine was proposed and applied. Comparison of the permeability coefficients produced from the two methods showed minimal difference. On this basis, the seepage field of the dam was analyzed. The analysis and field monitoring data reveal that the proposed algorithm is of high application value, which lays a foundation for future studies on the seepage properties of CFRD with cracks.
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