ArticleScientific reports2023
Prediction of ground vibration due to mine blasting in a surface lead-zinc mine using machine learning ensemble techniques.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Particle peak velocity prediction based on risk-oriented hybrid ensemble learning.Scientific reports · 2026Article
- Enhanced forecasting of friction and cohesion of augmented unsaturated soil with nanostructured quarry fines (NQF) addition.Scientific reports · 2026Article
- Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis.Scientific reports · 2026Article
- Predicting peak particle velocity in pre-splitting of gas-producing devices using improved particle swarm optimization algorithm.Scientific reports · 2025Article
- Adaptability evaluation model and experiment of full section SBM in deep strata based on AHP-fuzzy theory.Scientific reports · 2025Article
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- Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models.Scientific reports · 2024Article
- Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.Gels (Basel, Switzerland) · 2024Article
- Assessment of the ground vibration during blasting in mining projects using different computational approaches.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead-zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.