公路隧道施工期围岩快速分级的一种新方法  

FAST CLASSIFICATION METHOD FOR ROCK MASS SURROUNDING HIGHWAY TUNNEL DURING CONSTRUCTION

作　　者：方昱[1,2] 刘开云[1] 刘保国[1] 

机构地区：[1]北京交通大学土建学院北京100044 [2]安徽省高速公路控股集团有限公司合肥230088

出　　处：《工程地质学报》2013年第2期190-198,共9页Journal of Engineering Geology

基　　金：中央高校基本科研业务费项目(2011JBM267);交通运输部部省联合科技攻关项目(2009353334400)

摘　　要：施工期围岩快速分级是保证隧道施工安全和工程质量的关键措施。结合绩黄、宁绩高速公路隧道群施工期围岩分级实践,在大量现场测试和室内试验的基础上,提出了一种基于国标BQ分级的新分级体系,并给出了每个分级指标的现场快速测试方法。用新分级体系进行隧道施工期围岩的快速分级工作,并以分级结果作为进化支持向量回归算法分级的训练样本,建立了隧道围岩分级的进化支持向量回归智能模型。为了方便现场使用,依据支持向量回归理论,将智能模型进一步转化为初等函数数学模型,经隧道围岩分级实例验证了该初等函数数学模型的准确性,为隧道施工期围岩快速分级提供了一种简便的新方法。Fast classification of surrounding rock mass during construction period is the key measure for construction security and engineering quality.This paper combines the surrounding rock mass classification work during tunnel group construction in Jihuang and Ningji expressway.It proposes a new classification system as a kind of improved national standard BQ classification method.The system is based on many field tests and indoor experiments. The quick field testing method of each classification index is presented.Furthermore,the new classification system is used to cope with the surrounding rock classification in-situ.The classification results are served as the training samples of the evolutionary support vector regression(SVR) algorithm in order to establish the intelligent classification model.Finally,the intelligent model is transferred to an elementary function mathematical model according to SVR theory for the purpose of convenience application.The accuracy of this mathematical classification model is verified by classification examples of tunnel group.Thus,the system offers a new simple fast classification method for surrounding rock mass during tunnel construction period.

关 键 词：隧道工程 围岩快速分级 进化支持向量回归算法 智能模型 数学模型 

分 类 号：P642[天文地球—工程地质学]