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Gaussian process model of water inflow prediction in tunnel construction and its engineering applications

机译:隧道施工涌水量预测的高斯过程模型及其工程应用

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摘要

Due to the extremely complicated hydrogeological environment, significant symptoms of water inrush can not be detected accurately using normal exploratory methods, which produces hundreds of water inrushes occurred during tunnel construction in karst area. This study aims to present a new water inflow prediction technique without considering the relationship between hydrogeological features and water discharge rate. Therefore, the nonlinear regression Gaussian process analysis is applied to develop a model for predicting water inflow into tunnels. In order to meet the requirement of the data format of Gaussian process regression model (GPR), the basic evaluation index system of water inflow into tunnels and corresponding criterion are set up and quantified based on the statistical information of water inrush cases. To verify its feasibility, The GPR model is applied to Zhongjiashan tunnel on Jilian highway in China. The results of the comparisons indicate that the prediction results obtained from the GPR model are generally in a good agreement with the field-observed results. The proposed Gaussian process, on the whole, performs better than the support vector machine (SVM) and artificial neural network (ANN) in predictive analysis of water inflow into tunnels.
机译:由于极为复杂的水文地质环境,采用常规的勘探方法无法准确地发现明显的突水症状,在岩溶地区的隧道施工过程中会发生数百次突水。这项研究旨在提出一种新的水流预测技术,而无需考虑水文地质特征与排水率之间的关系。因此,应用非线性回归高斯过程分析建立了预测隧道入水量的​​模型。为了满足高斯过程回归模型(GPR)数据格式的要求,根据突水事件的统计信息,建立了隧道入水基本评价指标体系和相应的判据并进行了量化。为了验证其可行性,将GPR模型应用于中国吉连公路的钟家山隧道。比较的结果表明,从GPR模型获得的预测结果通常与现场观察到的结果吻合良好。总体而言,拟议的高斯过程在预测流入隧道的水量方面比支持向量机(SVM)和人工神经网络(ANN)更好。

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