摘要
地下水封洞库涌水量是评价工程质量的一个重要指标,目前工程中主要采用基于等效连续介质模型的各种方法进行预测分析,其预测值与实测值均存在明显的误差,难以满足实际工程需要。为提高水封洞库涌水量预测的准确性,在对影响涌水量的各种因素分析的基础上,利用人工神经网络(ANN)所具有的较强非线性映射能力和学习功能,建立水封洞库涌水量预测的非线性神经网络预测模型,并以国内已建的几个工程实测数据为训练样本。所建网络模型计算结果表明,采用ANN方法,预测分析简单快捷高效且预测精度高,具有良好的泛化性能,特别是预测过程中不再涉及复杂的理论模型和诸多难以确定的地质参数,是解决地下水封洞库涌水量预测的一种有效的方法。
Water yield is an important index to evaluate engineering quality for underground water-sealed rock cavern.The commonly used analysis prediction at precent is mainly based on equivalent continue media model,so its prediction value has a certain deviation against surveillance value,and not matching the engineering requirements.For the purpose of improving accuracy of prediction,based on analysis various factors impacting water yield,the nonlinear artificial neural network(ANN)model which was adopted to predict water yield of underground water-sealed rock cavern has been established,and real data from completed engineering in domestic was used as training dataset.The analysis prediction in this model shows that the ANN is a kind of simple,quick and convenient method with better accuracy and efficiency,which has good generalization performance,especially when dealing with some undetermined factors such as complicated theoretical model and geological parameters.It's an efficient and practical method to predict water yield of underground water-sealed rock cavern.
作者
何国富
张奇华
柳耀琦
沙裕
HE Guofu;ZHANG Qihua;LIU Yaoqi;SHA Yu(SINOPEC Shanghai Engineering Company Ltd.,Shanghai 200120,China;Badong National Observation and Research Station of Geohazards,China University of Geosciences,Wuhan,Hubei 430074,China)
出处
《水利与建筑工程学报》
2022年第5期30-34,74,共6页
Journal of Water Resources and Architectural Engineering
关键词
人工神经网络
地下水封洞库
网络训练
涌水量预测
artificial neural network
underground water-sealed rock cavern
network train
prediction of wa-ter yield
