基于RS-GP模型的边坡安全系数预测

Prediction of slope safety factor based on the RS-GP model

  • 摘要: 鉴于边坡系统影响因素之间的高度非线性和不确定性,融合RS-GP模型的优势,提出了依据边坡稳定性影响因素类比计算边坡安全系数的方法.该方法通过学习样本的数据特征分析、计算属性的重要性及约简规则,降低了遗传规划预测模型的结构规模.人工神经网络(ANN)模型与RS-GP模型计算结果比较表明:该方法具有计算速度快、容错能力强及精度高等特点.

     

    Abstract: Considering the high nonlinearity and uncertainty of influence factors in a slope system and making full use of the ad-vantages of the rough set theory and genetic programming (RS-GP model), a novel method based on the influencing factors of slope stability was brought up to calculate the safety factor of a slope. This method can reduce the structure scale of a genetic programming prediction model by analyzing the data characteristics of learning samples, calculating the significance of attributes and reducing rules. The results of an ANN model and the RS-GP model show that the proposed method has such merits as fast computing speed, high fault-tolerance capacity and high precision.

     

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