Subtopic Deep Dive

Blast-Induced Air Overpressure Analysis
Research Guide

What is Blast-Induced Air Overpressure Analysis?

Blast-Induced Air Overpressure Analysis studies predictive models and field measurements of air blasts from rock blasting in mining to control overpressure impacting nearby communities.

This subtopic focuses on empirical formulas and hybrid machine learning models like ANN-PSO for predicting blast-induced air overpressure and vibrations. Key studies use field data from quarries and mines to develop models such as optimized support vector regression. Over 30 papers exist on related blasting impacts, with recent works emphasizing uncertainty quantification.

6
Curated Papers
3
Key Challenges

Why It Matters

Accurate air overpressure prediction minimizes risks to human health and structures near open-pit mines, enabling safer blast designs. Huang et al. (2024) demonstrate optimized SVR models reducing prediction errors for vibration management. Paurush and Rai (2022) highlight field measurements guiding safe blasting practices in limestone quarries, protecting nearby inhabitants.

Key Research Challenges

Model Prediction Accuracy

Achieving precise forecasts of air overpressure from variable blast parameters remains difficult due to site-specific geology. Huang et al. (2024) used TSO, WOA, and CS optimizations on SVR to improve accuracy over traditional methods. Uncertainty in inputs like charge weight amplifies errors in open-pit scenarios.

Field Data Variability

Blasting measurements vary widely due to environmental factors, complicating model generalization. Paurush and Rai (2022) analyzed vibrations in limestone quarries, noting inconsistencies from rock types. Erten et al. (2022) applied deep ensembles to quantify uncertainty in granite quarries.

Real-Time Safety Integration

Incorporating overpressure models into real-time blast planning faces computational hurdles. Odeyemi et al. (2023) measured impacts from maximum instantaneous charge in Nigerian quarries. Ercins (2021) trained ANN on limited field data, limiting scalability.

Essential Papers

1.

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Yifan Huang, Zikang Zhou, Mingyu Li et al. · 2024 · Computer Modeling in Engineering & Sciences · 13 citations

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), an...

2.

Evaluation of ground vibrations induced by blasting in a limestone quarry

Punit Paurush, Piyush Rai · 2022 · Current Science · 12 citations

Despite being a versatile and low-cost method, rock blasting produces undesirable severe effects.The present study aims to examine the ground vibrations produced by blasting, which are of serious c...

3.

Blast-Induced Ground Vibration Prediction and Uncertainty Quantification in Granite Quarries Using Deep Ensembles Model

Gamze Erdogan Erten, Sinem Bozkurt Keser, Mahmut Yavuz · 2022 · 2 citations

<title>Abstract</title> Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-...

4.

Prediction of blast-induced ground vibrations in limestone mine using Multiple Linear Regression (MLR) Analysis

Arjun Ravikumar, Harsha Vardhan, K.V.S. Sarma · 2025 · Journal of Sustainable Mining · 1 citations

5.

Influence of explosive maximum instantaneous charge on blasting environmental impact

Olukemi Yetunde Odeyemi, Blessing Olamide Taiwo, Olarewaju Alaba · 2023 · Journal of Sustainable Mining · 1 citations

Our research looked at the effect of explosive maximum instantaneous charge on ground vibrations and noise levels during blasting operations at the Calaba limestone quarry in Nigeria. Vibrock (V900...

6.

Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

Serdar Ercins · 2021 · International Journal of Innovative Engineering Applications · 1 citations

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28...

Reading Guide

Foundational Papers

No pre-2015 foundational papers available; start with Ercins (2021) ANN predictions for baseline quarry vibration modeling.

Recent Advances

Huang et al. (2024) for SVR optimizations; Paurush and Rai (2022) for field evaluations; Erten et al. (2022) for uncertainty quantification.

Core Methods

Core techniques: Tuna Swarm Optimization on SVR (Huang et al., 2024), ANN training (Ercins, 2021), MLR analysis (Ravikumar et al., 2025), deep ensembles.

How PapersFlow Helps You Research Blast-Induced Air Overpressure Analysis

Discover & Search

Research Agent uses searchPapers and exaSearch to find Huang et al. (2024) on optimized SVR for blast vibrations, then citationGraph reveals Paurush and Rai (2022) connections, while findSimilarPapers uncovers Erten et al. (2022) deep ensembles.

Analyze & Verify

Analysis Agent applies readPaperContent to extract field data from Odeyemi et al. (2023), verifies predictions with verifyResponse (CoVe), and runs PythonAnalysis with NumPy/pandas to reanalyze vibration datasets, using GRADE grading for model reliability.

Synthesize & Write

Synthesis Agent detects gaps in ANN vs. empirical models across papers, flags contradictions in vibration predictions, while Writing Agent uses latexEditText, latexSyncCitations for Huang et al. (2024), and latexCompile to generate blast attenuation diagrams via exportMermaid.

Use Cases

"Reproduce ground vibration predictions from quarry data using Python."

Research Agent → searchPapers for Ercins (2021) → Analysis Agent → readPaperContent → runPythonAnalysis (pandas/matplotlib to fit ANN model) → researcher gets CSV export of predicted vs. measured vibrations.

"Write a LaTeX report comparing SVR and ANN blast models."

Synthesis Agent → gap detection on Huang et al. (2024) vs. Ercins (2021) → Writing Agent → latexEditText for sections → latexSyncCitations → latexCompile → researcher gets compiled PDF with overpressure figures.

"Find code implementations for blast vibration ML models."

Research Agent → searchPapers for Erten et al. (2022) → Code Discovery workflow (paperExtractUrls → paperFindGithubRepo → githubRepoInspect) → researcher gets inspected GitHub repos with deep ensemble code.

Automated Workflows

Deep Research workflow scans 50+ blasting papers via searchPapers, structures reports on overpressure models from Huang et al. (2024). DeepScan applies 7-step analysis with CoVe checkpoints to verify Paurush and Rai (2022) field data. Theorizer generates hypotheses on hybrid ANN-optimization for air blast attenuation.

Frequently Asked Questions

What is Blast-Induced Air Overpressure Analysis?

It involves modeling and measuring air blasts from rock blasting using empirical and ML methods to predict overpressure levels.

What are common methods used?

Methods include optimized SVR (Huang et al., 2024), ANN (Ercins, 2021), and deep ensembles (Erten et al., 2022) trained on field vibration data.

What are key papers?

Huang et al. (2024, 13 citations) on SVR optimization; Paurush and Rai (2022, 12 citations) on quarry vibrations.

What open problems exist?

Challenges include real-time integration and handling data variability across geologies, as noted in Odeyemi et al. (2023).

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