摘要
提出了用数据挖掘中的预测技术进行黄土湿陷性评价。根据实际工程资料建立黄土物理力学数据库,用BP算法建立预测模型。实例分析表明,预测湿陷系数所得的湿陷量计算值精度可达89%,这说明黄土湿陷性的智能化评价方法具有可行性和实用性。
This paper presents a method for assessment of loess collapsibility using the data mining technology. Coefficients of collapsibilities are predicted using the method. The database should be created based on practical engineering, and a prediction model, built with the BP neural network. The predicted loess collapse settlement is compared with the measured loess collapse settlement. Results show that prediction precision of collapse settlement is up to 89 % for a specific project case. This indicates that the intelligent method of evaluating loess collapsibility is very useful in engineering.
出处
《水土保持通报》
CSCD
北大核心
2006年第1期53-56,共4页
Bulletin of Soil and Water Conservation
基金
煤炭工业西安设计研究院科研基金资助
关键词
黄土湿陷性
数据挖掘
BP神经网络
预测
loess collapsibility
data mining
BP neural network
prediction
