天然岩体渗透系数反演研究

天然岩体存在很多裂隙，而水在这些裂隙中的渗流严重影响岩石工程的稳定性，因此确定天然岩体渗透系数具有重大的实际意义。反演方法是确定岩体渗透系数的一种较理想的方法，渗透系数反演可归结为一个复杂的非线性函数优化问题。由于采用传统优化技术存在不少问题，而目前采用的全局优化算法—遗传算法也存在本质的问题，因此，提出仿生算法-免疫进化规划进行岩体渗透系数反演，并用一个大坝坝基工程的算例证明了算法的有效性。结果表明，其方法可以在仅知道水头的条件下，得到接近实际的渗透系数值。

Back analysis of permeability coefficients for fractures of rock mass

There are many fractures in natural rock mass. The stability of rock engineering is severally affected by the seepage of water through these fractures. So, determination of the permeability coefficient of those fractures is very important. The back analysis is a suitable method to determine those parameters. But it is a complicated optimum problem of nonlinear function of back analysis of seepage parameters for rock mass. To solve this problem, there exist a lot of shortcomings to use traditional optimum methods, including genetic algorithm. So, a more suitable bionics algorithm, immunized evolutionary programming proposed by the author is used to solve this problem. At last, this new back analysis method is tested by a typical example. The results show that, the permeability coefficients by proposed algorithm are close to the real ones that can be gotten only through the known water head results.