Rockburst prediction and prevention in underground space excavation

Underground Space, Journal Year: 2023, Volume and Issue: 14, P. 70 - 98

Published: July 22, 2023

The technical challenges associated with deep underground space activities have become increasingly significant. Among these challenges, one major concern is the assessment of rockburst risks and instability rock masses. Extensive research has been conducted by numerous scholars to mitigate prevent occurrences through various methods. Rockburst incidents commonly occur during excavation hard in environments, posing severe threats personnel safety, equipment integrity, operational continuity. Thus, it crucial systematically document real cases rockburst, allowing for a comprehensive understanding underlying mechanisms triggering conditions. This will contribute advancement prediction prevention Proper selection an appropriate method fundamental aspect operations. However, there limited number studies that summarize compare different methods rockburst. paper aims address this gap analyzing global trends using CiteSpace software since 1990. It discusses classification characteristics, comprehensively reviews findings related prediction, including empirical, simulation, mathematical modeling, microseismic monitoring Additionally, presents compilation current measures. Notably, emphasizes significance control strategies, which provide key insights into effective utilization stored energy within rock. Finally, concludes suggesting six directions implementing intelligent management techniques hazards operations reduce probability incidents.

Language: Английский

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Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 591 - 591

Published: Feb. 22, 2024

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges its intricate cementitious matrix and vague mix design, where the components their relative amounts influence compressive strength. In response to these challenges, application of accurate applicable soft computing techniques becomes imperative for predicting strength such composite matrix. This research aimed predict using waste resources through novel ensemble ML algorithm. The dataset comprised 156 statistical samples, 15 variables were selected prediction. model employed combination RF, GWO algorithm, XGBoost. A stacking strategy was implemented by developing multiple RF models different hyperparameters, combining outcome predictions into new dataset, subsequently XGBoost model, termed RF–XGBoost model. To enhance accuracy errors, algorithm optimized hyperparameters resulting in RF–GWO–XGBoost proposed compared stand-alone models, hybrid GWO–XGBoost system. results demonstrated significant performance improvement strategies, particularly assistance exhibited better effectiveness, an RMSE 1.712 3.485, R2 0.983 0.981. contrast, (RF XGBoost) lower performance.

Language: Английский

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Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 36, P. 100819 - 100819

Published: July 21, 2022

Language: Английский

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COSMA-RF: New intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 36, P. 100806 - 100806

Published: July 8, 2022

Language: Английский

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Performance Evaluation of Rockburst Prediction Based on PSO-SVM, HHO-SVM, and MFO-SVM Hybrid Models

Mining Metallurgy & Exploration, Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 3, 2023

Language: Английский

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Decision tree models for the estimation of geo-polymer concrete compressive strength

Mathematical Biosciences & Engineering, Journal Year: 2023, Volume and Issue: 21(1), P. 1413 - 1444

Published: Jan. 1, 2023

<abstract> <p>The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring compressive strength geo-polymer (CSGPoC) needs a significant amount work and expenditure. Therefore, best idea is predicting CSGPoC with high level accuracy. To do this, base learner super machine learning models were proposed this study anticipate CSGPoC. The decision tree (DT) applied as learner, random forest extreme gradient boosting (XGBoost) techniques are used system. In regard, database was provided involving 259 data samples, which four-fifths considered for training model one-fifth selected testing models. values fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, 10/20 water/solids ratio, NaOH molarity input estimate evaluate reliability performance (DT), XGBoost, (RF) models, 12 evaluation metrics determined. Based on obtained results, highest degree accuracy achieved by XGBoost mean absolute error (MAE) 2.073, percentage (MAPE) 5.547, Nash–Sutcliffe (NS) 0.981, correlation coefficient (R) 0.991, R² 0.982, root square (RMSE) 2.458, Willmott's index (WI) 0.795, weighted (WMAPE) 0.046, Bias (SI) 0.054, p 0.027, relative (MRE) -0.014, a²⁰ 0.983 MAE 2.06, MAPE 6.553, NS 0.985, R 0.993, 0.986, RMSE 2.307, WI 0.818, WMAPE 0.05, SI 0.056, 0.028, MRE -0.015, 0.949 model. By importing set into trained 0.8969, 0.9857, 0.9424 DT, RF, respectively, show superiority estimation. conclusion, capable more accurately than DT RF models.</p> </abstract>

Language: Английский

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Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1285 - 1285

Published: Feb. 17, 2024

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through selection optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating strengths MEREC (Method for Eliciting Relative Weights) Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria discernment, weight determination using MEREC, prioritization framework. Fifteen ten sensors were identified, comprehensive analysis, including MEREC-based determination, led to “Ease Installation” as most critical criterion. Proximity identified choice, followed by biometric sensors, gas temperature humidity sensors. To validate effectiveness proposed model, rigorous comparison was conducted with established methods, VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, TRUST. encompassed relevant metrics such accuracy, sensitivity, specificity, providing understanding model’s performance relation other methodologies. outcomes this comparative analysis consistently demonstrated superiority model accurately selecting best ensuring mining. Notably, exhibited higher accuracy rates, increased improved specificity compared alternative These results affirm robustness reliability establishing it state-of-the-art decision-making framework mine safety. inclusion these actual enhances clarity credibility our research, valuable insights into superior existing main objective develop robust mines, focus on conditions. seeks identify prioritize context strives contribute mining industry offering structured effective approach selection, prioritizing operations.

Language: Английский

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Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

Buildings, Journal Year: 2024, Volume and Issue: 14(1), P. 225 - 225

Published: Jan. 14, 2024

The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass (RGP) on cement composites subjected an acidic setting. A dataset acquired from published literature was employed develop learning-based predictive models for mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), linear regression (LR) were chosen modeling. Also, RreliefF analysis performed relevance variables. total 234 data points train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, powder, 90 days CS considered as input outcomes research showed that could be applied evaluate reduction percentage in composites, including ESP RGP, after being exposed acid. Based R2 values (0.87 ANN, 0.81 KNN, 0.78 LR), well assessment variation between test anticipated errors (1.32% 1.57% 1.69% it determined accuracy ANN model superior KNN LR. sieve diagram exhibited a correlation amongst predicted target results. suggested RGP significantly influenced loss samples with scores 0.26 0.21, respectively. research, approach suitable predicting mortar environments, thereby eliminating lab testing trails.

Language: Английский

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Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques

Rock Mechanics and Rock Engineering, Journal Year: 2024, Volume and Issue: 57(9), P. 7535 - 7563

Published: May 14, 2024

Language: Английский

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Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes

Acta Geotechnica, Journal Year: 2022, Volume and Issue: 18(3), P. 1431 - 1446

Published: Sept. 2, 2022

Language: Английский

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