Prediction model for slope stability based on artificial immune algorithm
Abstract：Under the guidance of the simulation of antigen-antibody recognition in biological mechanisms of the immune system, based on the artificial immune algorithm, a prediction model for slope stability was introduced. The slope stability sample set was defined as antigen set, and the influence factors of slope stability were defined as the antibody. Through reiteration of genetic manipulation on the antigen gene segment, the antibody set that can perform slope stability well was developed. The affinity between prediction sample set and antibody set were calculated, and the KNN algorithm was used to predict the sample stability. The self-adaptive artificial immune algorithm was also employed to improve the model effectiveness and reliability. The case study confirms that self-adaptive artificial immune algorithm has a more accurate prediction than basic artificial immune algorithm, proving that the self-adaptive approach is effective. The new method can avoid from building complex ly reduce the modeling complexity and has better non-linear function between influence factors and stability, effectiveadaptability.
Keywords：artificial immune algorithmslope stabilityprediction modelself-adaptive
Publication Date：2012-06-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 911-917 )