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

Rock Mechanics and Rock Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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

Applied Sciences, Год журнала: 2024, Номер 14(14), С. 6164 - 6164

Опубликована: Июль 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.

Язык: 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, Год журнала: 2024, Номер 41(5), С. 2325 - 2340

Опубликована: Авг. 8, 2024

Язык: Английский

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Comprehensive review and future perspectives on prediction and mitigation of tunnel-induced ground settlement: A bibliometric analysis and methodological overview (2002–2022)

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 154, С. 106081 - 106081

Опубликована: Сен. 19, 2024

Язык: Английский

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Comparative analysis and application of rockburst prediction model based on secretary bird optimization algorithm

Frontiers in Earth Science, Год журнала: 2024, Номер 12

Опубликована: Дек. 16, 2024

The accurate rockburst prediction is crucial for ensuring the safety of underground engineering construction. Among various methods, machine learning-based can better solve nonlinear relationship between rockbursts and influencing factors thus has great potential applications. However, current research often faces certain challenges related to feature selection indices poor model optimization performance. This study compiled 342 cases from domestic international sources construct an initial database. In order determine relevant indicators, a method based on ReliefF-Kendall was proposed. database equalized visualized using Adasyn t-SNE algorithms. Five models [support vector (SVM), least-squares support (LSSVM), kernel extreme learning (KELM), Random Forest (RF), XGBoost] were established by employing Secretary Bird Optimization (SBO) algorithm 5-fold cross-validation optimize optimal selected comprehensive assessment generalization ability (accuracy, kappa, precision, recall, F1-score) stability (average accuracy). reliability proposed selection, optimization, data balancing methods verified comparing with other methods. results indicate that PSO-SVM demonstrated superior accuracy performance; reach 81.4% (optimal) 80.1% (average). main affecting occurrence are W et , maximum tangential stress ( MTS ), D uniaxial compressive strength UCS ). Finally, applied cases, achieving 90% verifying its applicability.

Язык: Английский

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

Arabian Journal for Science and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 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.

Язык: 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, Год журнала: 2025, Номер 11(1)

Опубликована: Май 22, 2025

Язык: Английский

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Gamma Mixture Model-based Domain Adaptation for semi-supervised rockburst risk recognition

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 163, С. 106646 - 106646

Опубликована: Июнь 3, 2025

Язык: Английский

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Indirect hazard evaluation by the prediction of backbreak distance in the open pit mine using support vector regression and chicken swarm optimization

Geohazard Mechanics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

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Predicting the hardgrove grindability index using interpretable decision tree-based machine learning models

Fuel, Год журнала: 2024, Номер 384, С. 133953 - 133953

Опубликована: Дек. 6, 2024

Язык: Английский

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