Enterprise AI Analysis
Statistical prediction method of inclined shaft blasting fragmentation based on dynamic damage distribution in excavated rock mass
This study addresses challenges in inclined shaft blasting excavation by proposing a novel statistical prediction method for rock fragmentation. It integrates field blasting experiments and LS-DYNA numerical simulations to establish a mapping between dynamic damage distribution and fragmentation gradation. The model achieves high accuracy (R²=0.9689) with a maximum field validation deviation of 2.49%, demonstrating strong engineering applicability. Key parameters—decoupling coefficient, blasthole spacing, and detonating delay time—were optimized. Results show that reducing the decoupling coefficient and decreasing blasthole spacing significantly enhance fragmentation and fine particle content, while detonating delay has a more limited, auxiliary role. The optimized parameters effectively reduce oversized fragments, improving blasting efficiency and economic viability for complex underground projects.
Executive Impact & Key Metrics
0.9689R²
Prediction Model Accuracy
2.49%
Max Field Validation Deviation
28.12%
Oversized Fragment Reduction
Problem Statement
Inclined shaft blasting excavation faces critical challenges, particularly pilot shaft blockage due to oversized rock fragments. This necessitates precise control over post-blast rock fragmentation to ensure smooth debris removal, avoid secondary blasting, and reduce manual clearing workloads, which are essential for the efficiency and economic viability of reverse pilot shaft expansion methods.
Solution Description
The proposed solution introduces a dynamic damage distribution-based prediction model for rock fragmentation in inclined shafts. By statistically mapping simulated damage distribution to observed fragmentation gradation, this method provides a data-efficient and mechanistically sound framework. It allows for optimized blasting parameters (decoupling coefficient, blasthole spacing, detonating delay) to achieve desired fragmentation, thereby preventing pilot shaft blockage and improving construction efficiency.
28.12%
Reduction in oversized fragments (boulder yield) post-optimization, ensuring smooth debris removal and reducing secondary blasting.
Deep Analysis & Enterprise Applications
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Blasting Fragmentation Control
This category focuses on methods and models for precisely controlling the size distribution of rock fragments after blasting. It covers empirical formulas, machine learning approaches, and novel dynamic damage distribution models designed to enhance prediction accuracy and optimize blasting parameters to prevent issues like oversized fragments and blockages.
Numerical Simulation & Material Modeling
This section delves into the application of advanced numerical simulation platforms (e.g., LS-DYNA) and material constitutive models (e.g., RHT model) to accurately represent rock mass behavior under explosive loads. It includes detailed parameter determination, meshing strategies, and boundary conditions for simulating dynamic damage evolution and fragmentation processes in complex geotechnical environments.
Underground Engineering & Optimization
This area addresses the practical challenges and solutions in inclined shaft excavation for projects like pumped storage power stations. It involves optimizing blasting designs, considering economic viability, operational safety, and specific construction requirements such as debris transportation and avoiding pilot shaft blockages. The focus is on translating theoretical models into implementable engineering strategies.
0.9689R²
The proposed damage distribution-based prediction model achieved very high fitting accuracy.
Enterprise Process Flow
Field Blasting Experiments
→
LS-DYNA Numerical Simulations
→
Dynamic Damage Distribution Analysis
→
Statistical Mapping Relationship
→
Fragmentation Prediction Model Development
→
Optimization of Blasting Parameters
| Method | Advantages | Limitations |
|---|---|---|
| Traditional Empirical Formulas (Kuz-Ram, CZM) |
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| Machine Learning Approaches (ANN, SVR) |
|
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| Proposed Dynamic Damage Distribution Model |
|
|
28.12%
Reduction in boulder yield achieved post-optimization, significantly improving mucking efficiency.
Tianchi Pumped Storage Power Station: Optimized Blasting Impact
The study was applied to the lower inclined shaft section of the No. 2 water diversion tunnel. Optimized parameters (decoupling coefficient k=1.31, detonating delay Δt=2ms, blasthole spacing s=0.69m) led to a significant reduction in oversized fragments. Specifically, the boulder yield decreased from 6.33% to 4.55%, a 28.12% reduction. This ensures smooth debris removal, prevents pilot shaft blockage, and improves overall construction efficiency and economic viability, validating the engineering applicability and promotional value of the proposed method.
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