摘要
海洋沉积物工程定名对于开展海洋工程建设具有重要作用,然而海底粉土和黏性土的定名受人为因素影响容易产生误差。使用人工神经网络的方法对黄河口埕岛海域284组细粒土数据进行了训练和学习,得到了只利用沉积物粒径质量分数进行定名的方法。结果表明,使用人工神经网络的方法能够有效地对沉积物进行工程定名。当网络含有5个输入层节点、9个隐藏层节点、3个输出层节点、训练函数为Scaled conjugate gradient时定名准确率最高,检验准确率高达97.7%。训练数据的数量是造成神经网络预测存在误差的重要因素,随着数据量的增加,网络的可靠性和通用程度将越来越高。
The engineering naming of marine sediments plays an important role in the development of marine engineering construction.However,the naming of silt and clay in the seabed is easy to be affected by human factors.Using the artificial neural network method,this study trained 284 sets of fine-grained soil data in Chengdao sea area of the Yellow River estuary and proposed a naming method using granularity data.Results showed that the method of artificial neural network performs well in fine submarine soil engineering naming.The accuracy of net naming is the highest,which as high as 97.7%,when the network contains 5 input layer nodes,9 hidden layer nodes,3 output layer nodes,and scaled conjugate gradient as the training function.The number of training data is an important factor that causes errors in neural network prediction.With increasing data volume,the reliability and universality of the network will become higher and higher.
作者
杜星
孙永福
宋玉鹏
张牧子
DU Xing;SUN Yong-fu;SONG Yu-peng;ZHANG Mu-zi(First Institute of Oceanography,MNR,Qingdao 266061,China;Marine Geology and Environment Laboratory Process,Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266235,China;National Deep Sea Center,Qingdao 266237,China)
出处
《海洋科学进展》
CAS
CSCD
北大核心
2021年第1期110-117,共8页
Advances in Marine Science
基金
山东省自然科学基金项目--波浪作用下海底沉积物内部孔压响应模拟试验技术研究(ZR2020QD073)
国家重点研发计划项目--南海多类型天然气水合物成藏原理与开采基础研究之水合物井场地层变形多场耦合计算及稳定性评价(2017YFC0307305)和海底沉积物力学特性的原位测试装置之装置海试与应用(2018YFC0309204)
关键词
人工神经网络
工程定名
海底细粒土
黄河口粉土
artificial neural network
engineering naming
seabed fine-grained soil
Yellow River Estuary silt