Neural network based prediction of ground surface settlements due to tunnelling
Neural network based prediction of ground surface settlements due to tunnelling
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DOI：
10.1016/s0266-352x(01)00011-8
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发表时间：
2001-01-01
影响因子：
5.3
通讯作者：
Shin, HS
中科院分区：
文献类型：
--
作者：
Kim, CY;Bae, GJ;Shin, HS
由于隧道开挖引起的地表沉降的大小和趋势取决于几个因素，如隧道几何形状，地面条件等，虽然有几个经验和半经验公式可用于预测地表沉降，其中大多数不同时考虑所有相关因素，导致不准确的预测。在这项研究中，人工神经网络（ANN）与“113”的现场监测结果预测地表沉降的隧道网站与规定的条件。为了实现这一点，一个标准的格式（协议）的数据库的监测现场数据首先提出，然后用于整理出各种监测数据集可在KICT。利用模式识别和记忆的能力。人工神经网络，试图捕捉丰富的物理特性涂抹在数据库中，并在同一时间过滤固有的噪声在监测数据。在这里，通过初步的参数研究，建议一个最佳的神经网络模型。它表明，根据给定的训练数据集生成一个最佳的人工神经网络的初步研究是必要的，因为没有为此目的的分析方法是迄今为止。此外，本研究还引入了相对效应强度（RSE）的概念[Yang Y，Zhang Q。应用人工神经网路进行岩石工程之阶层分析。岩石力学与岩石工程1997; 30（4）：207-22]对隧道施工中影响地表沉降的各种主要因素进行了敏感性分析。在一些例子中可以看到，RSE合理地使我们能够识别所有影响因素中最重要的因素。两个验证的例子进行了训练的人工神经网络使用本研究中创建的数据库。实例表明，人工神经网络已充分认识到监测数据集的特点，保持了进一步预测的一般性。本文提出的基于神经网络的分层预测方法可以进一步应用于具有不确定性和不完善性的岩土工程问题。(C)2001由Elsevier Science Ltd.出版。保留所有权利。
Ground surface settlement due to tunnel excavation varies in magnitude and trend depending on several factors such as tunnel geometry, ground conditions, etc. Although there are several empirical and semi-empirical formulae available for predicting ground surface settlement, most of these do not simultaneously take into consideration all the relevant factors, resulting in inaccurate predictions. In this study, an artificial neural network (ANN) is incorporated with '113' of monitored field results to predict surface settlement for a tunnel site with prescribed conditions. To achieve this, a standard format (a protocol) for a database of monitored field data is first proposed and then used for sorting out a variety of monitored data sets available in KICT. Using the capabilities of pattern recognition and memorization of the. ANN, an attempt is made to capture the rich physical characteristics smeared in the database and at the same time filter inherent noise in the monitored data. Here, an optimal neural network model is suggested through preliminary parametric studies. It is shown that preliminary studies for generating an optimal ANN under given training data sets are necessary because no analytical method for this purpose is available to date. In addition, this study introduces a concept of relative strength of effects (RSE) [Yang Y, Zhang Q. A heirarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering 1997; 30(4): 207-22] in sensitivity analysis for various major factors affecting the surface settlement in tunnelling. It is seen in some examples that the RSE rationally enables us to recognize the most significant factors of all the contributing factors. Two verification examples are undertaken with the trained ANN using the database created in this study. It is shown from the examples that the ANN has adequately recognized the characteristics of the monitored data sets retaining a generality for further prediction. It is believed that an ANN based hierarchical prediction procedure shown in this paper can be further employed in many kinds of geotechnical engineering problems with inherent uncertainties and imperfections. (C) 2001 Published by Elsevier Science Ltd. All rights reserved.