检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:曲嘉铭 袁超哲 陶润礼 孙文博 QU Jia-ming;YUAN Chao-zhe;TAO Run-li;SUN Wen-bo(CCCC National Engineering Research Center of Dredging Technology and Equipment Co.,Ltd.,Shanghai 201208,China)
机构地区:[1]中交疏浚技术装备国家工程研究中心有限公司,上海201208
出 处:《水运工程》2021年第1期196-201,共6页Port & Waterway Engineering
摘 要:福建沿海地区的吹填工程中,主要的土质颗粒为中粗砂。该类型土质在输送过程中阻力大较易发生堵管,导致施工进程延缓。在输送环境下施工,掌握管路内输送阻力的实时信息至关重要。粗颗粒条件下,常用的经验方法如Durand公式法的计算精度较差。基于已有的管路输送研究,以及福建沿海工程的测试数据,使用高斯过程回归方法和支持向量机方法建立管路压降预测模型。两种回归模型均能在训练期得到较为理想的效果,模型R^2指标达到0.80以上。在模型的预测期,支持向量机回归模型的R^2指标为0.78,高斯过程回归预测模型的R^2指标达到0.95。结果表明:基于高斯过程回归的机器学习模型能够较好地预测中粗砂吹填工程的疏浚参数。In the reclamation project in the coastal areas of Fujian,the main soil particles are medium-coarse sand types.In the process of transportation of this type of soil,large resistance is more likely to cause pipe blockage,which will delay the construction process.Under this working condition,it is very important to grasp the real-time information of the conveying resistance in the pipeline.However,under coarse particle conditions,commonly used empirical methods such as the Durand formula method have poor calculation accuracy.In this paper,based on the existing pipeline transportation research and the test data of the actual coastal engineering in Fujian,the Gaussian process regression method and support vector machine method are used to establish the prediction model of the pressure drop in the pipeline.Both regression models can get more ideal effects during the training period,the R^2 index of the model is above 0.80.In the prediction period of the model,the R^2 index of the support vector machine regression model is 0.78,and the R^2 index of the Gaussian process regression prediction model reaches 0.95.The results show that the machine learning prediction model based on Gaussian process regression can better predict the dredging parameters of the medium-coarse sand dredging project.
关 键 词:机器学习 排泥管段压降 支持向量机 高斯过程回归
分 类 号:U616[交通运输工程—船舶及航道工程]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.117