Abstract
This paper combined with the adaptive principle to improve the genetic algorithms (GA) and applied it to optimal design of the shape of the concrete face rock-fill dam (CFRD). Based on the improved GA, a mathematical model was established for the design optimization of CFRD. CFRD utilizes dam cost as objective function and dam slope and geometries of the dam material partition as design variables. Dam stability, stress, displacement, and stress level are used as the main condition constraints. The calculation procedures were prepared, and the GA was used to optimize the design of Jishixia CFRD. Results show that the GA could solve the global optimal solution problem of complex optimization design, such as the high degree of nonlinearity and the recessiveness of constraint conditions, and using the GA to optimize the CFRD design can reduce the quantities of projects and engineering safety costs.
Highlights
The objective function and constraint conditions for the design optimization of a concrete face rock-fill dam (CFRD) are nonlinear functions of design variables
This paper combined with the adaptive principle to improve the genetic algorithms (GA) and applied it to optimal design of the shape of the concrete face rock-fill dam (CFRD)
Results show that the GA could solve the global optimal solution problem of complex optimization design, such as the high degree of nonlinearity and the recessiveness of constraint conditions, and using the GA to optimize the CFRD design can reduce the quantities of projects and engineering safety costs
Summary
The objective function and constraint conditions for the design optimization of a concrete face rock-fill dam (CFRD) are nonlinear functions of design variables. Constraint conditions are implicit functions of design variables; deriving these constraint conditions is difficult [1]. The current design optimization methods for CFRD mainly focus on mathematical programming and criterion methods, which include the full stress criterion, complex method, and penalty function method. These methods have several limitations, such as slow convergence and low efficiency [2]. The continuity of the GA derivatives and functions has no limitations. GA has inherent implicit parallelism and good global optimization capability. GA uses the probability optimization method, which can automatically access and guide the optimized search space, can adjust the search direction adaptively, and does not require rule determination
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