What question did this study set out to answer?

This research aims to synthesize evidence on machine learning and AI uses in surface mining operations.

March 29, 2026Open Access

Machine Learning in Surface Mining—A Systematic Review

Key Points

This research aims to synthesize evidence on machine learning and AI uses in surface mining operations.
Conducted a systematic review of eligible peer-reviewed studies from 2020 to 2025.
Employed narrative synthesis due to dataset and algorithm heterogeneity.
Analyzed 5317 records, ultimately including 57 studies focusing on different mining operations.
Assessed risk of bias using a domain-based approach.
Identified gaps in reporting and validation practices within the studies.
Majority of studies centered on blasting (43), with fewer focusing on load and haul (6).
Statistical performance metrics like R2, RMSE, and MAE were commonly reported.
Operational performance indicators were limited.
Inconsistent reporting of dataset sizes was noted.
Significant potential for ML/AI was observed, especially in blasting operations.

Abstract

Objective: The objective of this study was to map and critically synthesize empirical evidence on ML/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020–2025) applying ML/AI to surface mining activities, training/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R2, RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study’s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software/tooling). Conclusions: ML/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605/OSF.IO/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727).

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Reis et al. (Fri,) studied this question.

synapsesocial.com/papers/69c8c43ede0f0f753b39efbb https://doi.org/https://doi.org/10.3390/app16073246

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