Mining method selection based on hierarchical clustering and correspondence analysis - Lower Silesian Digital Library

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Mining method selection based on hierarchical clustering and correspondence analysis

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Title: Mining method selection based on hierarchical clustering and correspondence analysis

Creator:

Kaykov, Dimitar ; Mijalkovski, Stojance ; Arsova-Borisova, Kremena

Description:

Mining Science, Vol. 32, 2025, s. 105-117

Abstrakt:

Selecting an optimal mining method is a complex and critical decision in underground mining, influenced by multiple geological, technical, and economic parameters. This study introduces a novel frame-work that combines Hierarchical Clustering (HC) and Correspondence Analysis (CA) to enhance the selec-tion process by evaluating the consistency and similarity among outcomes from both first-pass methods (UBC and Nicholas) and several multi-criteria decision-making (MCDM) techniques (including AHP, EDAS, PROMETHEE II, AHP-PROMETHEE, TOPSIS, and VIKOR). The proposed HC-CA approach identifies consistent conflicts among the considered mining methods and quantifies the agreement among the initial assumptions of the adopted selection procedures. A case study of a Pb-Zn deposit demonstrates that the framework can effectively detect consistent and co-occurring (i.e., conflicting) solutions, such as Cut-and-Fill Stoping, Shrinkage Stoping, and Sublevel Stoping. The results show that the adopted design criteria align more closely with the UBC selection method, compared to the Nicholas selection procedure for the considered deposit. Additionally, applying the HC-CA approach to the input matrices prior to applying the MCDM methods can yield different results, compared to subjecting the MCDM output scores to the proposed framework. This integrative approach extends traditional selection procedures and links them with commonly used MCDM methodologies and unsupervised machine learning methods by enabling flexible strategy development, with the inclusion of considering mixed-mining-method scenarios tailored to the deposit. Additionally, the approach offers improved decision support in early project stages by visualizing affinities among different assumptions and hence potentially mitigating biases during the following design stage.

Publisher:

Wroclaw University of Technology

Place of publication:

Date:

Resource Type:

Resource Identifier:

oai:dbc.wroc.pl:140066

Source:

click here to follow the link

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Relation:

Mining Science ; Mining Science, Vol. 32, 2025 ; Prace Naukowe Instytutu Gornictwa Politechniki Wroclawskiej ; Politechnika Wrocławska. Wydział Geoinżynierii, Górnictwa i Geologii

Rights:

Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy

Access Rights:

Dla wszystkich zgodnie z licencją

License:

CC BY-NC 3.0 ; click here to follow the link

Location:

Wroclaw University of Technology

Group publication title:

Subject and keywords:

mining geology geo-engineering

Object collections:

Last modified:

Sep 16, 2025

In our library since:

Sep 16, 2025

Number of object content hits:

26

All available object's versions:

https://dlibra.kdm.wcss.pl/publication/178899

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Mining Science

Edition name	Date
Mining method selection based on hierarchical clustering and correspondence analysis	Sep 16, 2025

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