An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Июль 1, 2024

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis critical span graph must be updated meet more stringent engineering requirements. Given this, this study introduces support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) optimization (Harris hawks (HHO), cuckoo search (CS)) techniques, overcome limitations traditional methods. The indicates that hybrid model combining SVM, bagging, CS strategies has good prediction performance, its test accuracy reaches 0.86. Furthermore, partition scheme is adjusted based on CS-BSVM 399 cases. Compared previous empirical or semi-empirical methods, new overcomes interference subjective factors possesses higher interpretability. Since relying solely one technology cannot ensure credibility, further genetic programming (GP) kriging interpolation techniques. explicit expressions derived through GP offer probability value, technique provide interpolated definitions two subclasses. Finally, platform developed above three approaches, which rapidly feedback.

Язык: Английский

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Discussion on “Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi optimization Strategies and GP Technique” Rock Mech Rock Eng (2024)

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

Опубликована: Янв. 10, 2025

Язык: Английский

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Reply to Discussion by Adoko on “Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi‑optimization Strategies and GP Technique” Rock Mech Rock Eng, 57, 9781–9804

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

Опубликована: Фев. 2, 2025

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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Integrating Advanced Machine Learning Models for Accurate Prediction of Porosity and Permeability in Fractured and Vuggy Carbonate Reservoirs: Insights from the Tarim Basin, Northwestern, China

SPE Journal, Год журнала: 2025, Номер unknown, С. 1 - 27

Опубликована: Апрель 1, 2025

Summary Accurate prediction of porosity and permeability in fractured vuggy carbonate reservoirs is crucial for optimizing hydrocarbon recovery but remains challenging due to their extreme heterogeneity anisotropy. Traditional methods often struggle capture the complex geological variability, leading suboptimal reservoir characterization. To address this, we propose a novel hybrid machine learning (ML) framework that integrates particle swarm optimization (PSO), mixed-effects random forest (MERF), ensemble models, such as light gradient boosting (LightGBM), (XGBoost), (RF). These models were trained validated using leave-one well-out cross-validation (LOO-CV) train-test split method, leveraging geophysical well-log data from Tarim Basin’s reservoirs. Among three PSO-MERF-LightGBM outperformed others, achieving an R² 0.9752 root mean square error (RMSE) 0.0606 R2 0.9983 RMSE 0.00473 during testing. Moreover, model demonstrates exceptional computational efficiency, completing processing just 11 seconds 9 seconds, respectively. This marks significant reduction computation time compared with other making it highly efficient alternative. results confirm its superior ability nonlinear relationships spatial variability. The study how advanced ML techniques can enhance characterization, improving decision-making subsurface resource management. Future research should extend this settings validate broader applicability.

Язык: Английский

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Identification and Stability Analysis of Mine Goafs in Mineral Engineering Based on Multi-Survey Data

Sensors, Год журнала: 2025, Номер 25(9), С. 2776 - 2776

Опубликована: Апрель 28, 2025

Unregulated underground group mining in China has led to problems such as unclear locations and complex shapes of mine goafs mineral engineering, posing serious safety hazards for subsequent operations. This paper takes engineering with the research object, integrates multi-survey data from surface deformation remote sensing monitoring 3D laser scanning measurement survey area where rate reaches 14cm/ year, accurately identifies location risky goafs, constructs detailed representations real inside engineering. The FLAC3D 6.0 software is used establish a numerical simulation model fully considering process, conducting characteristic analysis stress distribution, failure range response revealing impact void on stability mine. results are combined on-site investigations verify whether geological disasters have been caused by goafs. methods can provide effective technical means assessment which help reduce risk mines improve

Язык: Английский

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Advancing dynamic reliability assessment of reservoir slopes using attention-based neural networks

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(6)

Опубликована: Май 29, 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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