南华大学学报（自然科学版）2026，Vol.40Issue(2)：22-31,10.DOI:10.19431/j.cnki.1673-0062.2026.02.003
湖南安化抽水蓄能电站围岩氡析出主控因素及其预测
Main controlling factors and prediction of radon exhalation from surrounding rocks of Anhua pumped storage power station,Hunan
摘要
Abstract
The issue of radon exhalation from rock masses has become increasingly promi-nent during the construction of underground powerhouse facilities in pumped storage power stations,posing a long-term health threat to construction and operational personnel.Meas-ured data from the exploratory adit of the Anhua Pumped Storage Power Station in Hunan Province indicate that deep radon concentrations significantly exceed allowable limits,ne-cessitating effective prevention and control measures.This study developed a prediction model for radon exhalation rates through experimental investigations on samples collected from the adit combined with machine learning methods.The results show that:(1)Lithol-ogy,temperature,humidity,atmospheric pressure,and water content significantly influ-ence the radon exhalation rate.Lithology determines the order of magnitude of the radon exhalation rate.Increasing temperature and humidity promote radon exhalation,while a high-pressure environment has an inhibitory effect.Under saturated water content condi-tions,the radon exhalation rate increases significantly,confirming the"radon carrier"effect of water molecules.(2)Grey relational analysis indicates that lithology and water content are the dominant factors controlling the radon exhalation rate.(3)Prediction mod-els were constructed based on Support Vector Machine(SVM)and Back Propagation(BP)neural networks,and were optimized using the Particle Swarm Optimization(PSO)algorithm.Among these,the SVM model exhibited the best performance,achieving a co-efficient of determination(R2)of 0.964 46 and a root mean square error(ERMSE)of 1.533 1×10-3 on the training set.After optimization,the ERMSE of the PSO-BP model was reduced by 9.8%compared to the BP model.The research findings can provide theoret-ical support and a technical basis for radon prevention and control in pumped storage power stations.关键词
抽水蓄能电站/氡析出率/主控因素/机器学习/预测模型Key words
pumped storage power station/radon exhalation rate/controlling factors/machine learning/predictive model分类
资源环境引用本文复制引用
韩世礼,邓梓翔,肖健,贺海洋,谢焱石..湖南安化抽水蓄能电站围岩氡析出主控因素及其预测[J].南华大学学报（自然科学版）,2026,40(2):22-31,10.基金项目
湖南省自然科学基金项目(2023JJ30506) （2023JJ30506）
