摘要
首蓄期准确预测坝体变形,合理安排蓄水计划对于特高拱坝安全进入运行期具有重要意义。为解决特高拱坝首蓄期坝体变形预测较难的问题,本文提出了一种特高拱坝首蓄期坝体变形预测混合模型方法,并结合白鹤滩特高拱坝首蓄期坝体变形监测资料进行工程实例验证。该模型结合白鹤滩特高拱坝首蓄期三阶段蓄水计划的背景,结合三个蓄水目标下白鹤滩拱坝拱冠梁坝段正垂测点的监测数据量大小,在首蓄期初期采用多元回归模型,在首蓄期中后期对PLdb18-2到PLdb18-6五个测点采用优化的LSTM模型,对于坝顶的PLdb18-1采用多元回归模型。本文针对混合模型及全过程采用单一模型的预测结果和实测结果进行对比,本文提出的混合模型方法精度最高,误差率不超过4%,且具有较好鲁棒性。
Accurate prediction of the dam deformation during initial impoundment and a reasonable water impounding scheme are of great significance to the safe transition of a new extra-high arch dam into normal operation.To overcome the difficulties in the prediction,this paper presents a hybrid model method for the predictions of a very high arch dam,and validates it against the monitoring data of initial impounding deformation of Baihetan dam.In this method,we consider a three-stage scheme for the initial impoundment of the dam and the size of monitoring data from its crown beam section required by this scheme.It adopts two models-a multiple regression model for the first stage,and an optimized LSTM model and this regression model for five measurement points of PLdb18-2 to PLdb18-6 and the dam crown point PLdb18-1 respectively in the middle and last stages.Predictions using this hybrid model and a single model are verified against the measurements,and it shows our hybrid model method is robust and more accurate with an error lower than 4%.
作者
隗轶伦
胡昱
王亚军
谭尧升
刘春风
裴磊
WEI Yilun;HU Yu;WANG Yajun;TAN Yaosheng;LIU Chunfeng;PEI Lei(School of Ocean Engineering Equipment,Zhejiang Ocean University,Zhoushan,Zhejiang 316000;State Key Laboratory of Hydroscience and Engineering,Tsinghua University,Beijing 100084;China Three Gorges Group Corporation,Beijing 100038;China Three Gorges Construction Engineering Corporation,Chengdu 610041)
出处
《水力发电学报》
CSCD
北大核心
2022年第5期84-92,共9页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51839007,51979145)
中国长江三峡集团公司科研项目资助(BHT/0809)。
关键词
水利工程
特高拱坝
首蓄期
变形
多元回归
LSTM
混合模型
hydraulic engineering
extra-high arch dam
initial impounding period
deformation
multiple regression
LSTM
hybrid model
