Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

Natural Resources Research, Journal Year: 2024, Volume and Issue: 33(5), P. 2037 - 2062

Published: June 19, 2024

Language: Английский

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Methodology for Constructing Explicit Stability Formulas for Hard Rock Pillars: Integrating Data-Driven Approaches and Interpretability Techniques

Rock Mechanics and Rock Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Language: Английский

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Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques

Underground Space, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi-optimization Strategies and GP Technique

Rock Mechanics and Rock Engineering, Journal Year: 2024, Volume and Issue: 57(11), P. 9781 - 9804

Published: July 11, 2024

Language: Английский

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Enhanced multi-task learning models for pile drivability prediction: Leveraging metaheuristic algorithms and statistical evaluation

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 47, P. 101288 - 101288

Published: June 3, 2024

Language: Английский

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Borehole Breakout Prediction Based on Multi-Output Machine Learning Models Using the Walrus Optimization Algorithm

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6164 - 6164

Published: July 15, 2024

Borehole breakouts significantly influence drilling operations’ efficiency and economics. Accurate evaluation of breakout size (angle depth) can enhance strategies hold potential for in situ stress magnitude inversion. In this study, borehole is approached as a complex nonlinear problem with multiple inputs outputs. Three hybrid multi-output models, integrating commonly used machine learning algorithms (artificial neural networks ANN, random forests RF, Boost) the Walrus optimization algorithm (WAOA) techniques, are developed. Input features determined through literature research (friction angle, cohesion, rock modulus, Poisson’s ratio, mud pressure, radius, stress), 501 related datasets collected to construct dataset. Model performance assessed using Pearson Correlation Coefficient (R2), Mean Absolute Error (MAE), Variance Accounted For (VAF), Root Squared (RMSE). Results indicate that WAOA-ANN exhibits excellent stable prediction performance, particularly on test set, outperforming single-output ANN model. Additionally, SHAP sensitivity analysis conducted model reveals maximum horizontal principal (σH) most influential parameter predicting both angle depth breakout. Combining results studies analyses conducted, considered be an effective size.

Language: Английский

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Mean Block Size Prediction in Rock Blast Fragmentation Using TPE-Tree-Based Model Approach with SHapley Additive exPlanations

Mining Metallurgy & Exploration, Journal Year: 2024, Volume and Issue: 41(5), P. 2325 - 2340

Published: Aug. 8, 2024

Language: Английский

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Ground Settlement Prediction in Urban Tunnelling: Leveraging Metaheuristic-Optimized Random Forest Models

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract With the continuous acceleration of urbanization, problem ground settlement induced by underground tunnel construction has received more and widespread attention. This study addresses challenge predicting surface subsidence in urban construction, a critical concern geotechnical engineering. Random forest (RF) models were optimized using three distinct metaheuristic algorithms: ant lion optimizer (ALO), multiverse (MVO), grasshopper optimization algorithm (GOA). The enhancements significantly improved model accuracy, as demonstrated detailed performance metrics GOA-optimized RF (GOA-RF Pop = 20) on Changsha Metro Line 3 dataset, which included 294 instances 12 feature parameters. achieved an MAE 1.3820, MAPE 181.2249, correlation coefficient 0.9273, RMSE 2.5209 training set; 2.4695, 275.2054, R value 0.8877, 4.2540 testing set. A sensitivity analysis within random framework revealed that torque (To) condition (Gc) had most significant impact subsidence, whereas influence modified dynamic penetration test (MDPT) was least pronounced. Additionally, MATLAB-based application developed App Designer module, integrating these into user-friendly GUI facilitates prediction management risks, thereby enhancing practical effectiveness engineering risk mitigation strategies.

Language: Английский

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Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Journal Year: 2025, Volume and Issue: 11(1)

Published: May 22, 2025

Language: Английский

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Simulation Study on Rock Crack Expansion in CO2 Directional Fracturing

Processes, Journal Year: 2024, Volume and Issue: 12(9), P. 1813 - 1813

Published: Aug. 26, 2024

In underground construction projects, traversing hard rock layers demands concentrated CO2 fracturing energy and precise directional crack expansion. Due to the discontinuity of mass at tip prefabricated fractures in fracturing, traditional simulations assuming continuous media are limited. It is challenging set boundary conditions for high strain rate large deformation processes. The dynamic expansion mechanism 3D fracture network not yet fully understood. By treating stress waves as hemispherical resonance using a particle loading method along with condition processing, numerical model constructed. This analyzes propagation spatial materials. results show that undirected relies on weak structures near borehole, whereas guided, extending fracture’s range. Additionally, vital re-expansion by high-pressure gas, leading formation symmetrical, umbrella-shaped structure evenly developed fractures. findings also demonstrate discrete element (DEM) effectively reproduces each stage providing basis studying cracking mechanism.

Language: Английский

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