New method to identify optimal discontinuity set number of rock tunnel excavation face orientation based on Fisher mixed evaluation

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Keshen Zhang , Wei Wu , Min Zhang , Yongsheng Liu , Yong Huang , Baolin Chen
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引用次数: 0

Abstract

Discontinuity is critical for strength, deformability, and permeability of rock mass. Set information is one of the essential discontinuity characteristics and is usually accessed by orientation grouping. Traditional methods of identifying optimal discontinuity set numbers are usually achieved by clustering validity indexes, which mainly relies on the aggregation and dispersion of clusters and leads to the inaccuracy and instability of evaluation. This paper proposes a new method of Fisher mixed evaluation (FME) to identify optimal group numbers of rock mass discontinuity orientation. In FME, orientation distribution is regarded as the superposition of Fisher mixed distributions. Optimal grouping results are identified by considering the fitting accuracy of Fisher mixed distributions, the probability monopoly and central location significance of independent Fisher centers. A Halley-Expectation-Maximization (EM) algorithm is derived to achieve an automatic fitting of Fisher mixed distribution. Three real rock discontinuity models combined with three orientation clustering algorithms are adopted for discontinuity grouping. Four clustering validity indexes are used to automatically identify optimal group numbers for comparison. The results show that FME is more accurate and robust than the other clustering validity indexes in optimal discontinuity group number identification for different rock models and orientation clustering algorithms.

基于费舍尔混合评价的岩石隧道开挖面方位最佳间断集数识别新方法
不连续性对岩体的强度、变形性和渗透性至关重要。集合信息是基本的不连续特征之一,通常通过方位分组来获取。传统的确定最佳不连续度集合数的方法通常通过聚类有效性指标来实现,这种方法主要依赖于聚类的聚集性和分散性,导致评价的不准确性和不稳定性。本文提出了一种新的费雪混合评价(FME)方法来确定岩体不连续方位的最优组数。在 FME 中,方位分布被视为 Fisher 混合分布的叠加。通过考虑费舍尔混合分布的拟合精度、独立费舍尔中心的概率垄断性和中心位置重要性,确定最佳分组结果。推导出一种 Halley 期望最大化(EM)算法,以实现 Fisher 混合分布的自动拟合。采用三种真实岩石不连续性模型结合三种方位聚类算法进行不连续性分组。使用四个聚类有效性指标来自动识别最佳分组数进行比较。结果表明,对于不同的岩石模型和方位聚类算法,FME 比其他聚类有效性指标在最佳不连续面组号识别方面更准确、更稳健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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