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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 the process of tunnel construction, in order to accurately and promptly classify the surrounding rock rapidly, the BP neural network method is introduced to establish the fast grading parameter standard to classify the tunnel face that has been excavated according to the classification criteria of the surrounding rock of the tunnel and measure its Rapid grading parameters, the results of surrounding rock grading and its corresponding rapid grading parameters to establish BP neural network training set, so as to obtain the surrounding rock grading model. Finally, the rapid grading parameters of excavating tunnel face are measured and provided to the model for discrimination, so as to achieve the purpose of rapid and accurate grading. Through the tunnel samples of a tunnel verified by examples, the results of this model are consistent with the actual construction conditions, which can be used to guide the rapid classification of tunnel surrounding rock during construction.