Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques

Kahraman, Esma; Hosseini, Shahab; Taiwo, Blessing Olamide; Fissha, Yewuhalashet; Jebutu, Victor Afolabi; Akinlabi, Adams Abiodun; Adachi, Tsuyoshi

doi:10.1016/j.jsasus.2024.05.001

Journal of Safety and Sustainability

2024

DOI: 10.1016/j.jsasus.2024.05.001

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Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques

Esma Kahraman,

Shahab Hosseini,

Blessing Olamide Taiwo

et al.

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2024

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Cited by 2 publications

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Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

Dong,

Zhang,

Chen

et al. 2024

Sci Rep

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The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments.

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Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

Dong,

Zhang,

Chen

et al. 2024

Sci Rep

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Methods for Assessing the Layered Structure of the Geological Environment in the Drilling Process by Analyzing Recorded Phase Geoelectric Signals

Abzhanova,

Bykov,

Surzhik

et al. 2024

Mathematics

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Assessment of the current state of the near-surface part of the geological environment and understanding of its layered structure play an important role in various scientific and applied fields. The presented work is devoted to the application of phasometric modifications of geoelectric control methods to solve the problem of the detailed complex study of the underground layers of the environment in the process of drilling operations with the use of special equipment. These studies are based on the analysis of variations in phase parameters and characteristics of an artificially excited multiphase electric field to assess poorly distinguishable details and changes in the layered structure of the medium. The proposed method has increased accuracy, sensitivity and noise proofness of measurements, which allows for extracting detailed information about the heterogeneity, composition and stratification of underground geological formations not only in the zone where the drill makes contact with the medium, but also in the entire control zone. This paper considers practical mathematical models of phase images for basic scenarios of drill penetration between the layers of the near-surface part of the geological medium with different characteristics, obtained by means of approximation apparatus based on continuous piecewise linear functions, and also suggests the use of modern machine learning methods for intelligent analysis of its structure. Studying the phase shifts in electrical signals during drilling highlights their value for understanding the dynamics of soil response to the process. The observed signal changes during the drilling cycle reveal in detail the heterogeneity in soil structure and its response to changes caused by drilling. The stability of phase shifts at the last stages of the process indicates a quasi-equilibrium state. The results make a significant contribution to geotechnical science by offering an improved approach to monitoring a layered structure without the need for deep drilling.

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Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques

Cited by 2 publications

References 44 publications

Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

Methods for Assessing the Layered Structure of the Geological Environment in the Drilling Process by Analyzing Recorded Phase Geoelectric Signals

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