摘要
在隧道施工过程中,为准确、及时地进行围岩快速分级,引入BP神经网络方法,通过制定快速分级参数标准,对已经开挖的隧道工作面按照隧道围岩分级规范进行分级,并测量其快速分级参数,将围岩分级的结果与其对应的快速分级参数建立BP神经网络的训练集合,从而得到围岩分级模型。最后测量正在开挖隧道工作面的快速分级参数,并提供给模型进行判别,从而达到快速、精确分级目的。通过某隧道围岩样品实例验证,该模型判断结果与实际施工情况吻合,可用于指导施工阶段的隧道围岩快速分级。
In order to achieve a rapid,accurate classification of surrounding rock in tunnel construction,a tunnel face that has been excavated is firstly graded according to the classification standard of surrounding rock and its corresponding grading parameters are measured.Then,the grading results and their corresponding parameters are used as a study sample set to train a BP neural network,and a rapid grading model for surrounding rock is obtained.Thus,if the grading parameters measured in tunnel site are put in the model,the model can make a rapid judgment of the grades of the surrounding rock.Actual application shows that this model can precisely work out the grade of surrounding rock and can be applied to direct the rapid classification of surrounding rock in construction stage.
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2010年第2期41-45,共5页
China Safety Science Journal
基金
国家重点基础研究("973")项目(2002CB412702)
国家自然科学基金重大项目资助(50490271)
关键词
快速分级
分级参数
BP神经网络
训练集合
隧道围岩
rapid classificationl
grading parameters
BP neural network
training set
surrounding rock
