河海大学学报（自然科学版）2025，Vol.53Issue(5)：127-135,9.DOI:10.3876/j.issn.1000-1980.2025.05.015
基于PCA-AVOA-LightGBM的混凝土坝应力预测模型
Stress prediction model of concrete dam based on PCA-AVOA-LightGBM
常留红 1朱勇 1曾子彬 1尹光景 2高宏宇 1邬传峰1
作者信息
- 1. 长沙理工大学水利与环境工程学院,湖南长沙 410114||长沙理工大学水沙科学与水灾害防治湖南省重点实验室,湖南长沙 410114
- 2. 中国水利水电第八工程局有限公司科研设计院,湖南长沙 410004
- 折叠
摘要
Abstract
Based on the principal component analysis(PCA)method,the African vulture optimization algorithm(AVOA),and lightweight gradient learning machine(LightGBM)model,a stress prediction model of concrete dam based on PCA-AVOA-LightGBM was constructed.PCA method was used to mine the main influencing factors of dimension reduction-based stress prediction,and AVOA was introduced to optimize the hyperparameters of the LightGBM model.Based on the stress monitoring data of a concrete dam,the PCA method was applied to vector regression machine(SVR),random forest(RF),extreme gradient boosting(XGboost),LightGBM models,and a comparative analysis was conducted with the PCA-AVOA-LightGBM model.The results show that the PCA method effectively reduces the multicollinearity among the influencing factors of each model.The PCA-AVOA-LightGBM model shows better performance in accuracy and efficiency than other prediction models and can be applied in stress monitoring of similar concrete dams.关键词
混凝土坝/超参数/应力预测/主成分分析方法/非洲秃鹫优化算法/极端梯度提升Key words
concrete dam/hyperparameter/stress prediction/principal component analysis/African vulture optimization algorithm/extreme gradient boosting分类
建筑与水利引用本文复制引用
常留红,朱勇,曾子彬,尹光景,高宏宇,邬传峰..基于PCA-AVOA-LightGBM的混凝土坝应力预测模型[J].河海大学学报（自然科学版）,2025,53(5):127-135,9.
