TBM净掘进速率预测模型及多指标评价方法研究

Prediction model and multi-index evaluation method for TBM penetration rate

为了建立巷道TBM(全断面隧道掘进机)施工净掘进速率预测模型,基于马来西亚Pahang-Selangor隧洞通过室内试验和现场记录获取的100组岩体力学和TBM掘进参数,采用统计回归分析、人工神经网络、机器学习和多算法融合等方法建立了17个TBM净掘进速率(PR)预测模型,提出了一种新的归一多指标模型评价方法,对模型的各个评价指标依次进行倾向一致性转换、归一化、求和与排序,同时使用归一法和已有的排名法对比分析17个模型的预测性能,结果表明:多算法融合提高了人工神经网络和分类与回归树模型的预测能力,略微降低了支持向量回归模型的预测能力;分类与回归树模型和基于分类与回归树的多算法融合模型预测能力最好,更适用于PR预测;提出的归一法为不同模型的多指标综合评价提供了量化方法,克服了排名法在模型预测能力差别较小时无法精确识别的不足。

In order to establish a tunnel TBM(full-face tunnel boring machine)penetration rate prediction model,based on 100 sets of rock mechanics and TBM excavation parameters obtained through indoor tests and field records in the Pahang-Selangor tunnel in Malaysia.First,17 TBM penetration rate(PR)prediction models were established using statistical regression analysis(SRA),artificial neural network(ANN),machine learning(ML)and multi-algorithm fusion method.Then,a new normalized multi-index model evaluation method is proposed.Each evaluation index of the model,in turn,is treated through conversion of the orientation consistency,normalization,summation and ranking,and the normalization method and the existing ranking method are used to compare and analyze the performance of the 17 prediction models,the results show that:the multi-algorithm fusion improves the prediction ability of artificial neural networks and classification and regression tree models,but slightly reduces the prediction ability of support vector regression models;classification and regression tree model and multi-algorithm fusion model based on classification and regression tree have the best prediction ability and are more suitable for PR prediction;the proposed normalization method provides a quantitative way for multi-index comprehensive evaluation between different models,and overcomes the shortage of traditional ranking method that cannot accurately identified when the difference of model prediction ability is relatively small.