.png){kind=icon}
返回
TBM performance estimation using a classification and regression tree (CART) technique
.png){kind=icon}
PRE
AI
DOI：10.1007/s10064-016-0969-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict the advance rate of an excavation. In this study, a database of actual machine performance from T05 and T06 tunnels of the deep tunnel sewerage system (DTSS) project in Singapore which include: rock mass uniaxial compressive strength, brittleness index (B (i)), volumetric joint account (J (v)), joint orientation (J (o)), TBM specifications and corresponding TBM performance has been compiled. Then, for prediction of specific rock mass boreability index (SRMBI), two different models including classification and regression tree (CART) analysis and multivariate regression analysis (MVRA) have been developed. As statistical indices, correlation coefficient (R (2)), root mean square error (RMSE) and variance accounted for (VAF) were used to evaluate the efficiency of the developed models for determining the SRMBI of TBMs. According to the obtained results, it was observed that the performance of the CART model is better than the MVRA.
Keyword:
Penetration rate
TBM performance
Specific rock mass boreability index (SRMBI)
CART
MVRA
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
5.2K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏Prediction of blast-induced ground vibration using artificial neural networks用人工神经网络预测爆炸引起的地面振动
PREAI
分享收藏分享收藏
查看更多内容