盾构滚刀磨损的影响因素敏感性分析及预测

摘要/Abstract

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doi:10.6040/j.issn.1672-3961.0.2023.320

摘要： 以汕头湾海底隧道盾构段的监测数据为基础,基于数理统计法和机器学习法探究不同影响因素对滚刀磨损的敏感性程度。确定4个对滚刀磨损有明显影响的因素,分别为掘进距离、转速、转程和旋转角;建立以上述4个影响因素作为神经网络输入节点的反向传播神经网络(backpropagation neural network, BPNN)的滚刀磨损预测模型,采用遗传算法(genetic algorithm, GA)对预测模型进行优化并开展预测研究。结果表明:相较于BP模型,GA-BP模型的决定系数R2从0.706 4提高到0.823 9,预测精度明显提升,说明GA能够明显提高BPNN模型的预测能力。

关键词: 滚刀磨损, 敏感性分析, 数理统计, 反向传播神经网络, 遗传算法

中图分类号:

TU94

孙尚渠,张恭禄,蒋志斌,李朝阳. 盾构滚刀磨损的影响因素敏感性分析及预测[J]. 山东大学学报 (工学版), 2025, 55(1): 86-96.

SUN Shangqu, ZHANG Gonglu, JIANG Zhibin, LI Zhaoyang. Sensitivity analysis of influencing factors and prediction of shield disc cutter wear[J]. Journal of Shandong University(Engineering Science), 2025, 55(1): 86-96.

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