|本期目录/Table of Contents|

[1]冯宝俊,刘敦文,褚夫蛟.基于PSO-SVM模型的隧道水砂突涌量预测研究[J].中国安全生产科学技术,2014,10(7):123-129.[doi:10.11731/j.issn.1673-193x.2014.07.022]
　FENG Bao jun,LIU Dun wen,CHU Fu jiao.Study on prediction of water and sand inrush quantity in tunnel based on PSOSVM model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(7):123-129.[doi:10.11731/j.issn.1673-193x.2014.07.022]
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基于PSO-SVM模型的隧道水砂突涌量预测研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年7期
页码:
123-129
栏目:
职业安全卫生管理与技术
出版日期:
2014-07-31

文章信息/Info

Title:
Study on prediction of water and sand inrush quantity in tunnel based on PSOSVM model
作者:
冯宝俊; 刘敦文; 褚夫蛟
（中南大学 资源与安全工程学院，湖南 长沙 410083）
Author(s):
FENG Bao jun;  LIU Dun wen;  CHU Fu jiao
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
公路隧道; 水砂突涌; PSO-SVM; 预测分析
Keywords:
road tunnel;  water and sand inrush;  PSOSVM;  prediction and analysis
分类号:
X947
DOI:
10.11731/j.issn.1673-193x.2014.07.022
文献标志码:
A
摘要:
复杂工程地质条件下，隧道水砂混合物突涌的预测防控是隧道安全建设的基础，准确预测水砂混合物突涌量，为工程提供安全保障至关重要。为提高预测准确性，提出一种基于粒子群算法优化的支持向量机(PSO-SVM)的隧道水砂突涌量预测模型。综合考虑地质构造、气象条件、施工影响三类因素，选取七个因子，结合某公路隧道，利用PSO-SVM建立隧道水砂突涌量预测模型，并对该隧道水砂突涌量进行预测，结果与实际突涌量一致。证实综合粒子群算法和支持向量机优势的PSO-SVM方法预测精度高，且易于实现，为类似隧道工程突涌预测提供参考与借鉴
Abstract:
The prediction and prevention of water and sand mixture inrush in tunnel under complicated geological conditions is the foundation of tunnel safety construction. Predicting the inrush quantity of water and sand mixture accurately is quite important for providing safety support to engineering. In order to improve the prediction accuracy, the forecasting model for inrush quantity of water and sand mixture based on support vector machine combined with particle swarm algorithm optimization (PSOSVM) was presented. Taking a road tunnel as engineering background, the geological structure, meteorological conditions and construction influence factors were selected as the major elements by considering of seven determiners, the forecasting model of tunnel water and sand inrushing was established based on PSOSVM. The prediction process by the model was conducted and the wellpleasing results were acquired. The results showed that the comprehensive method can effectively improve the performance of prediction. Based on above conclusion, PSOSVM is an approving method, and easily to be implemented, which provides a significant technical mean for prediction of water and sand mixture inrush in tunnel, and presents notably useful reference value for engineering practice.

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备注/Memo

备注/Memo:
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更新日期/Last Update: 2014-07-30