Jie Cao

The antifloating property of underground structures in areas with high underground water levels is a key design aspect. Evaluating the buoyancy forces acting on underground structures is complicated, particularly in the presence of confined water beneath the structures. Herein, the effects of the permeability coefficient of layered soil, hydraulic gradient, and embedment depth of the aquiclude on the buoyancy force acting on underground structures are investigated through three model tests: calibration of the test system, buoyancy force acting on a structure located in homogeneous soil considering vertical direction seepage, and buoyancy force acting on a structure located in layered soil considering vertical seepage of confined water. The results show that the pore pressure along the structure and the buoyancy force acting on the underground structure considering seepage are greater than those obtained under hydrostatic conditions. The raising ratios of the pore pressure and buoyancy force are equal to the vertical hydraulic gradient when seepage occurs in homogeneous soil. In the presence of confined water, the raising ratio is significantly greater than the hydraulic gradient. In the cases studied herein, the raising ratio is approximately twice the hydraulic gradient. Simplified equations are proposed to calculate the buoyancy force acting on underground structures considering the vertical seepage of confined water. Finally, a finite element analysis is carried out to verify the conclusions obtained from the model test and the rationality of the proposed equations.

Parallel attribute reduction is one of the most important topics in current research on rough set theory. Although some parallel algorithms were well documented, most of them are still faced with some challenges for effectively dealing with the complex heterogeneous data including categorical and numerical attributes. Aiming at this problem, a novel attribute reduction algorithm based on neighborhood multigranulation rough sets was developed to process the massive heterogeneous data in the parallel way. The MapReduce-based parallelization method for attribute reduction was proposed in the framework of neighborhood multigranulation rough sets. To improve the reduction efficiency, the hashing Map/Reduce functions were designed to speed up the positive region calculation. Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed. The effectiveness and superiority of this parallel algorithm were demonstrated by theoretical analysis and comparison experiments.