开采地面沉陷预测的自适应神经模糊推理方法研究  

An ANFIS-based Approach for Predicting Mining Induced Surface Subsidence

作　　者：丁德馨[1] 张志军[1] 毕忠伟[1] 

机构地区：[1]南华大学建筑工程与资源环境学院

出　　处：《中国工程科学》2007年第1期33-39,共7页Strategic Study of CAE

基　　金：国家自然科学基金资助项目(50274043);湖南省自然科学基金重点资助项目(01JJY1004);湖南省教育厅重点资助项目(01A015)

摘　　要：现行各种开采地面沉陷预测方法均存在着一个共同的缺陷,均不能在集成以往开采地面沉陷工程实例的基础上对某一地下采矿工程所引起的地面沉陷进行预测,而只能根据某种物理的或力学的方法对其进行预测。人类在工程实践中所创造的开采地面沉陷方面的经验是非常宝贵的财富,应当在建立开采地面沉陷预测方法时加以充分利用。以所收集的开采地面沉陷工程实例为基础,应用自适应神经模糊推理系统对他们进行了集成,建立了开采地面沉陷预测的自适应神经模糊推理方法,并进而采用工程实例对该方法进行了检验。结果表明,该方法具有收敛速度快、拟合能力强、推广预测精度高、解的稳定性好等优点,是一种优异的开采地面沉陷预测方法。Current approaches for predicting mining induced surface subsidence have a drawback in common that they predict the subsidence only on the basis of a physical or mechanical approach irrespective of the practical examples in engineering practice in mining induced surface subsidence. However, these experiences created in engineering practice are of great value and full use should be made of them to establish an approach for predicting mining induced surface subsidence. Therefore, this paper accumulated a lot of practical examples of mining induced surface subsidence, integrated these examples by using adaptive neuro-fuzzy inference system （ANFIS） and established an ANFIS-based approach for predicting mining induced surface subsidence. The approach was further tested by using practical examples of mining induced surface subsidence. The results show that the approach can converge quickly, fit the data in very good agreement and make generalization prediction with high accuracy.

关 键 词：地下开采 开采地面沉陷 自适应神经模糊推理系统 

分 类 号：TD325.4[矿业工程—矿井建设]