An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data

被引：59

作者：

Jung, Jee-Hee ^{[1
]}

Chung, Heeyoung ^{[1
]}

Kwon, Young-Sam ^{[1
]}

Lee, In-Mo ^{[1
]}

机构：

[1] Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 02841, South Korea

来源：

KSCE JOURNAL OF CIVIL ENGINEERING | 2019年 / 23卷 / 07期

关键词：

artificial neural network (ANN); backpropagation (BP) algorithm; tunnel boring machine (TBM); TBM data; tunnel face; ground condition prediction; ground types; PERFORMANCE;

D O I：

10.1007/s12205-019-1460-9

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper presents an artificial neural network (ANN) model that predicts ground conditions ahead of a tunnel face by using shield tunnel boring machine (TBM) data obtained during the tunneling operation. The primary advantage of the proposed technique is that, by using TBM data, no additional data acquisition device is required. Ground type classifications and machine data normalization methods are introduced to maintain the consistency of the measured data and improve prediction accuracy. The efficacy of the proposed model is demonstrated by its 96% accuracy in predicting ground type one ring ahead of the tunnel face.

引用

页码：3200 / 3206

页数：7

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