基于遗传规划的复杂地层中盾构滚刀磨损寿命预测

乔金丽, 孟秋杰, 刘建琴, 金建星

乔金丽,孟秋杰,刘建琴,等.基于遗传规划的复杂地层中盾构滚刀磨损寿命预测[J].工矿自动化,2018,44(9):51-58.. DOI: 10.13272/j.issn.1671-251x.2018020048
引用本文: 乔金丽,孟秋杰,刘建琴,等.基于遗传规划的复杂地层中盾构滚刀磨损寿命预测[J].工矿自动化,2018,44(9):51-58.. DOI: 10.13272/j.issn.1671-251x.2018020048
QIAO Jinli, MENG Qiujie, LIU Jianqin, JIN Jianxing. Prediction of wear life of shield disc cutter in complex formations based on genetic programming[J]. Journal of Mine Automation, 2018, 44(9): 51-58. DOI: 10.13272/j.issn.1671-251x.2018020048
Citation: QIAO Jinli, MENG Qiujie, LIU Jianqin, JIN Jianxing. Prediction of wear life of shield disc cutter in complex formations based on genetic programming[J]. Journal of Mine Automation, 2018, 44(9): 51-58. DOI: 10.13272/j.issn.1671-251x.2018020048

基于遗传规划的复杂地层中盾构滚刀磨损寿命预测

基金项目: 

国家自然科学基金项目(5167050969)

详细信息
  • 中图分类号: TD67

Prediction of wear life of shield disc cutter in complex formations based on genetic programming

  • 摘要: 针对复杂地层盾构掘进过程中滚刀磨损量大、使用寿命难以预测且影响滚刀磨损寿命因素具有多元性和不确定性等问题,分析了复杂地层中安装半径、掘进距离、贯入度、刀间距、刀盘转速等主要的滚刀寿命影响因素,建立了基于遗传规划的复杂地层中盾构滚刀磨损寿命预测模型。该模型通过遗传规划将磨损寿命预测问题转化为程序的归纳问题,通过树状函数表达式反映出在复杂地质条件下滚刀寿命影响因素与磨损寿命之间的关系。工程测试结果表明,该模型平均预测误差为16.07%,均方差为0.001 6,均优于简化CSM模型,有效解决了滚刀寿命预测难的问题,为滚刀寿命预测提供了新的解决方法。
    Abstract: In process of shield tunneling in complex formation, wear of disc cutter is extremely serious and service life is hard to predict, and influence factors of the disc cutter wear are multi-dimensional and uncertain. In view of above problems, main influence factors of disc cutter wear life such as cutter installation raduis, excavation distance, penetration depth, cutter spacing width, rotating speed were analyzed, prediction model of wear life of shield disc cutter in complex formations based on genetic programming was established. Genetic programming can transform the problem of wear life prediction into the inductive problem of programs. The tree-shaped expression can reflect relationship between the influencing factors and the wear life under complex geological conditions. The engineering test results show that the average prediction error of the model is 16.07% and the mean square error of the model is 0.001 6, which are better than the simplified CSM model. The model solves the problem that the wear life of the disc cutter is difficult to predict, and provides a new solution for prediction of wear life of the disc cutter.
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    其他类型引用(15)

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出版历程
  • 刊出日期:  2018-09-09

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