摘要
为挖掘混凝土大坝变形监测数据与各影响因素之间复杂的非线性关系,提高特高拱坝变形预测精度,在孪生支持向量机(TWSVM)模型基础上,引入位置因子与速度因子,运用自适应粒子群优化(APSO)算法进行参数优化,构建了特高拱坝变形的APSO-TWSVM预测模型。实例验证结果表明,该模型可有效挖掘拱坝变形与影响因子间复杂的非线性关系,模型运算速度和精度均比传统SVM模型有明显提升。
In order to explore the complex non-linear relationship between concrete dam deformation monitoring data and various influencing factors,and to improve the deformation prediction accuracy of ultra-high arch dams,based on the twin support vector machine(TWSVM)model and the adaptive particle swarm optimization(APSO)method for parameter optimization,an APSO-TWSVM prediction model for the deformation of a very high arch dam was constructed by introducing position and velocity factors.Case analysis shows that the model can effectively explore the complex non-linear relationship between the deformation of arch dams and the influencing factors,and compared with the traditional SVM method,the model speed and accuracy have been significantly improved.
作者
张才溢
傅蜀燕
欧斌
胡孟凡
王春华
ZHANG Caiyi;FU Shuyan;OU Bin;HU Mengfan;WANG Chunhua(College of Water Conservancy,Yunnan Agricultural University,Kunming 650201,China)
出处
《水利水电科技进展》
CSCD
北大核心
2023年第4期46-51,共6页
Advances in Science and Technology of Water Resources
基金
国家自然科学基金项目(52069029)
云南省教育厅科学研究基金项目(2023J0519)。
关键词
特高拱坝
变形预测
孪生支持向量机
自适应粒子群优化算法
ultra-high arch dam
prediction model
twin support vector machine
adaptive particle swarm optimization method
作者简介
张才溢(1998—),男,硕士研究生,主要从事大坝安全监测研究。E-mail:1481982393@qq.com;通信作者:欧斌(1983—),男,副教授,博士,主要从事水工结构安全监测诊断研究。E-mail:oubin418@126.com/oubin@ynau.edu.cn。
