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Retrieval Model of Slope Stability Evaluation System Based on Cluster Analysis and Genetic Algorithm

机译:基于聚类分析和遗传算法的坡稳定性评价系统检索模型

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Case-based reasoning technique is one of the artificial intelligence methods developed recently. This paper studies the retrieval model of slope stability evaluation system based on Case-based reasoning. Aimed at existent problem of K-Nearest Neighbor strategy (KNN), dynamic cluster method is used to organize index for slope cases, and the cases are classified into different typical sub-base cases according to property or failure style of slope, which could contract case retrieval space and reducing retrieval time. Through analyzing the influence degree on slope stability evaluation result of each factor and its historic data, genetic algorithm combined KNN is adopted to optimize weight, and a rather objective weight value could be denoted for each attribute into increase quality. Practical engineering slopes are applied to test the retrieval model system. And the results show that cluster analysis method could raise retrieval efficiency, and the optimizing calculation by genetic algorithm combined KNN for weight is objective, effective, and simple. And this retrieving model could raise retrieval efficiency and accuracy of slope case stability evaluation system.
机译:基于案例的推理技术是最近开发的人工智能方法之一。本文研究了基于基于案例推理的边坡稳定性评估系统检索模型。旨在存在K-Collect邻策略(KNN)的问题,动态群集方法用于组织斜率索引的索引,并且根据斜率的属性或故障方式分类为不同的典型子基础情况,可以合同案例检索空间和减少检索时间。通过分析每个因素及其历史数据的坡度稳定性评估结果的影响程度,采用遗传算法组合KNN优化权重,并且可以为每个属性表示相当的客观重量值,以提高质量。应用实用的工程斜率来测试检索模型系统。结果表明,集群分析方法可以提高检索效率,并通过遗传算法组合重量的优化计算是客观,有效,简单。该检索模型可以提高斜率稳定性评估系统的检索效率和准确性。

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