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Study on the GA-ANIFIS intelligence model for nonlinear displacement time series analysis of long and large tunnel construction

机译:长大隧道施工非线性位移时间序列分析的GA-ANIFIS智能模型研究

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摘要

Long and large tunnel construction has become the emphasis and difficulty in worldwide. The deformation controlling of surrounding rock is the key to ensure construction safety. But, deformation prediction during construction period is a very difficult problem because of its strong nonlinear feature of displacement time series. Meanwhile, the traditional methods can’t be used to forecast the displacement of long and large tunnel accurately. The adaptive neuro-fuzzy inference system (ANFIS) has a remarkable ability of learning and generalization and genetic algorithm (GA) is an excellent global optimization tool. So, GA was used to optimize the parameters of ANFIS and the corresponding calculation code has been programmed in this paper.The intelligence prediction model based on GA-ANFIS was established combined with displacement monitoring of BAOZHEN long and large tunnel in Yichang-Wanzhou railway. Compared the prediction results with that of GA-BP algorithm, it could be concluded that GA-ANFIS model can obtain a higher precision than GA-BP algorithm in deformation prediction of long and large tunnel during construction period which offered reference for similar engineering.
机译:大而长的隧道建设已成为世界范围内的重点和难点。围岩的变形控制是保证施工安全的关键。但是,由于位移时间序列具有很强的非线性特征,因此施工期的变形预测是一个非常困难的问题。同时,传统方法无法准确预测长大隧道的位移。自适应神经模糊推理系统(ANFIS)具有出色的学习和泛化能力,而遗传算法(GA)是出色的全局优化工具。因此,利用遗传算法对ANFIS的参数进行优化,并编写了相应的计算代码。结合万宝铁路宝贞长大隧道位移监测,建立了基于GA-ANFIS的智能预测模型。将预测结果与GA-BP算法的预测结果进行比较,可以得出结论:GA-ANFIS模型在长大隧道施工期的变形预测中可以获得比GA-BP算法更高的精度,为类似工程提供参考。

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