Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
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DOI：
10.1016/j.engappai.2009.03.007
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发表时间：
2009-06-01
影响因子：
8
通讯作者：
Iplikci, S.
中科院分区：
文献类型：
--
作者：
Yagiz, S.;Gokceoglu, C.;Iplikci, S.
隧道掘进机（TBM）性能预测是实现全断面掘进机机械化施工的关键。许多模型和方程先前已被引入到估计TBM性能的岩石和机器的属性的基础上，采用各种统计分析技术。然而，考虑到问题的性质，它是相对困难的估计隧道掘进机的性能线性预测模型。人工神经网络（ANN）和非线性多元回归模型具有很大的潜力，建立这样的预测模型。本研究的目的是非线性多变量预测模型的建设，以估计作为岩石性质的函数的TBM性能。为此，岩石性质和机器数据收集最近完成的TBM隧道项目在美国纽约市，因此，数据库的建立，开发性能预测模型，利用人工神经网络和非线性多元回归方法。本文介绍了非线性预测方法的应用研究结果，提供了可接受的精确性能估计。(C)2009爱思唯尔有限公司保留所有权利。
Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations. (C) 2009 Elsevier Ltd. All rights reserved.