Geological identification using shield tunneling parameters based on four unsupervised clustering methods

IF 6.7 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Wei-bin Chen , Chengyu Hong , Yan Guo , Haijun Wang , Xiangsheng Chen , Xiaojie Xue , Shuhua Huang
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引用次数: 0

Abstract

This study develops a comprehensive framework for identifying geological characteristics (GC) during shield tunneling. Four unsupervised clustering methods are employed: K − means++, Fuzzy C − means (FCM), Gaussian Mixture Model (GMM), and Hierarchical Clustering (HIC). These clustering methods are applied to both the original and dimensionally reduced datasets, which are obtained through principal component analysis (PCA). The performance of each clustering method is emphasized by capturing the inherent variability of GC. K − means++ and FCM exhibit high recognition rates (89.54 % and 90.37 % respectively) and excellent stability. The mean Rand index values for both the original and PCA − processed data are close to 1. PCA significantly enhances the performance of GMM. The identification rate of GMM increases from 67.28 % to 87.68 %, and its mean Rand index improves towards 1. Conversely, HIC has low recognition rates (18.63 % for the original data and 20.60 % after PCA) and low mean Rand index values (0.93 for the original data and 0.91 after PCA), indicating poor stability. The proposed framework, integrating data preprocessing, comprehensive index calculation, and the application of clustering algorithms, provides a robust and effective approach to improve the identification and understanding of GC during the tunneling process. This, in turn, can enhance decision − making processes in similar engineering projects.
基于四种无监督聚类方法的盾构参数地质识别
本研究建立了盾构施工地质特征识别的综合框架。采用了四种无监督聚类方法:K - means++、模糊C - means (FCM)、高斯混合模型(GMM)和分层聚类(HIC)。这些聚类方法适用于原始数据集和降维数据集,这些数据集是通过主成分分析(PCA)获得的。通过捕获GC的固有可变性来强调每种聚类方法的性能。K−means++和FCM具有较高的识别率(分别为89.54%和90.37%)和良好的稳定性。原始数据和PCA处理数据的Rand指数平均值都接近于1。PCA显著提高了GMM的性能。GMM的识别率由67.28%提高到87.68%,平均Rand指数向1提高。相反,HIC的识别率较低(原始数据为18.63%,PCA后为20.60%),Rand指数值平均值较低(原始数据为0.93,PCA后为0.91),稳定性较差。该框架集成了数据预处理、综合索引计算和聚类算法的应用,为提高挖掘过程中GC的识别和理解提供了一种鲁棒有效的方法。这反过来又可以提高类似工程项目的决策过程。
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来源期刊
Tunnelling and Underground Space Technology
Tunnelling and Underground Space Technology 工程技术-工程:土木
CiteScore
11.90
自引率
18.80%
发文量
454
审稿时长
10.8 months
期刊介绍: Tunnelling and Underground Space Technology is an international journal which publishes authoritative articles encompassing the development of innovative uses of underground space and the results of high quality research into improved, more cost-effective techniques for the planning, geo-investigation, design, construction, operation and maintenance of underground and earth-sheltered structures. The journal provides an effective vehicle for the improved worldwide exchange of information on developments in underground technology - and the experience gained from its use - and is strongly committed to publishing papers on the interdisciplinary aspects of creating, planning, and regulating underground space.
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