CAJ | 학술논문

围岩类别超前分类是隧道施工过程中必须开展的一项工作，其直接关系到后续的开挖及施工支护方案。为有效地进行隧道围岩类别超前分类，提出了基于数字钻进技术和量子遗传（QGA）-径向基函数（RBF）神经网络的围岩类别超前分类方法。以数字钻进技术为基础，从钻进参数中提取有用信息，构建围岩类别超前分类指标体系。采用量子计算原理对遗传算法进行改进，通过量子位编码和量子旋转门更新种群，以此来确定RBF神经网络的参数，建立了基于QGA-RBF神经网络的围岩类别超前识别系统。最后将该方法应用于青岛胶州湾海底隧道的围岩类别超前识别中，结果表明，该方法具有较高的准确性，其结果为围岩类别超前分类提供了一种新思路。
위암유별초전분류시수도시공과정중필수개전적일항공작，기직접관계도후속적개알급시공지호방안。위유효지진행수도위암유별초전분류，제출료기우수자찬진기술화양자유전（QGA）-경향기함수（RBF）신경망락적위암유별초전분류방법。이수자찬진기술위기출，종찬진삼수중제취유용신식，구건위암유별초전분류지표체계。채용양자계산원리대유전산법진행개진，통과양자위편마화양자선전문경신충군，이차래학정RBF신경망락적삼수，건립료기우QGA-RBF신경망락적위암유별초전식별계통。최후장해방법응용우청도효주만해저수도적위암유별초전식별중，결과표명，해방법구유교고적준학성，기결과위위암유별초전분류제공료일충신사로。
Conducting the advanced surrounding rock classification is a necessary working in the process of tunnel construction, which is directly related to subsequent excavation and supporting construction scheme. In order to conduct surrounding rock classification ahead tunnel advancing effectively, the advanced surrounding rock classification method based on digital drilling technology and quantum genetic algorithm (QGA)-radical basis function (RBF) neural network is put forward. The method extracts useful information from the drilling parameters;and it establishes the indicators system of advanced surrounding rock classification. In the progress of establishing advanced surrounding rock classification index system based on QGA-RBF neural network, the genetic algorithm are improved by quantum calculation principle;and the parameters of RBF neural network could be determined by quantum bit and rotation gate renew population. Finally, the method is applied to the subsea tunnel across Qingdao Jiaozhou bay engineering. The results show that the method has higher prediction accuracy and provides a new idea in advanced prediction of surrounding rock classification.