摘要
在综合分析开采影响下建筑物损坏程度影响因素的基础上,采用自适应BP神经网络技术建立了建筑物采动损坏程度的预测模型。以大量的建筑物采动损坏实例作为学习训练样本和测试样本,对模型预测结果与实际值进行了对比分析。结果表明,用人工神经网络方法预测建筑物采动损害程度是可行的。为开采影响下建筑物损坏程度预测和评价探索出了一种新的方法。
The main factors affecting the mining-induced damage degree of buildings are comprehensively analyzed. Then the model is established to predict the damage degree of buildings by applying the theory of artificial neural network(ANN). Based on a large amount of cases related to buildings damaged by mining,the predicted results of the model and the measured values are compared and analyzed. The results show that it is feasible to predict the mining-induced damage degree of buildings by ANN technology.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2004年第4期583-587,共5页
Chinese Journal of Rock Mechanics and Engineering
基金
河南省优秀中青年骨干教师基金项目
河南省教育厅自然科学基金(2003440222)资助项目。
关键词
地下工程
采动损害
建筑物
神经网络
智能预测
人工神经网络
underground engineering,mining-induced damage,building,artificial neural networks,intelligent prediction
