露天矿边坡工程地质分区的单值嗜中性亲和传播方法

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Jibo Qin , Xiaoming Sun , Shigui Du , Jun Ye
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引用次数: 0

摘要

工程地质区划是评价露天矿边坡稳定性的重要依据。由于地质条件的复杂性和不确定性,这项工作给采矿工程师和研究人员带来了巨大的挑战。本文提出了一种用于OPM边坡工程地质区划的单值中性亲和性传播(SVNS-AP)方法。该方法利用单值嗜中性集(SVNS)的概念,通过真、不确定和假隶属函数来表达工程地质条件影响因素中存在的不一致和不确定信息。然后,将SVNS的相似度测度整合到关联传播(affinity propagation, AP)算法中,计算数据点之间的相似度。最后,利用改进的剪影指数对聚类结果进行评价,确定最优聚类数。兰坪铅锌OPM工程地质分区的实际应用结果表明,SVNS-AP方法是不确定环境下OPM边坡工程地质分区的有效方法。基于文献中的数据集和UC Irvine Machine Learning Repository的聚类结果表明,所提出的方法可以作为一种通用的聚类算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A single-valued neutrosophic affinity propagation approach for engineering geological zoning of open-pit mine slopes
Engineering geological zoning is an important foundation for assessing the stability of open-pit mine (OPM) slopes. Because of the complexity and uncertainty of geological conditions, this work brings great challenges to mining engineers and researchers. This paper presents a single-valued neutrosophic affinity propagation (SVNS-AP) approach for engineering geological zoning of OPM slopes. The approach utilizes the concept of a single-valued neutrosophic set (SVNS) to express the inconsistent and indeterminate information present in the influencing factors of engineering geological conditions through truth, indeterminate and falsity membership functions. Then, the similarity measure of SVNS is integrated into the affinity propagation (AP) algorithm to calculate the degree of similarity between the data points. Finally, the modified silhouette index is used to evaluate the clustering results and decide the optimal number of clusters. The practical application results of the engineering geological zoning of Lanping lead-zinc OPM demonstrate that the SVNS-AP method is an effective way for engineering geological zoning of OPM slopes in the uncertain environment. Clustering results based on datasets in the literature and the UC Irvine Machine Learning Repository show that the proposed method can be used as a general clustering algorithm.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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