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在综合分析隧道周边位移影响因素的基础上,采用基于Takagi-Sugeno(T-S)模型的自适应神经模糊推理系统(ANFIS)建立了隧道周边位移预测模型。运用将军山隧道施工监控数据作为学习训练样本和测试样本,通过对预测模型的预测值与实测值进行对比来判断预测模型的稳定性。结果表明:自适应神经模糊推理系统预测隧道周边位移有较高的精度,可为隧道周边位移监控提供一种新的有效预测方法。
Based on the comprehensive analysis of the influencing factors of tunnel periphery displacement, the tunnel peripheral displacement prediction model is established by the adaptive neuro-fuzzy inference system based on Takagi-Sugeno (T-S) model (ANFIS). By using the data of construction monitoring of JJ mountain tunnel as training samples and test samples, the stability of prediction model can be judged by comparing the predicted value and the measured value of the prediction model. The results show that the adaptive neuro-fuzzy inference system can predict the displacement around the tunnel with high accuracy, which can provide a new effective prediction method for the displacement monitoring around the tunnel.