Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review DOI Creative Commons
Bingbing Yu, Bo Wang, Yuantong Zhang

и другие.

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Авг. 19, 2024

The deep integration of computer field and coal mining is the only way to mine intellectualization. A variety artificial intelligence tools have been applied in open-pit shallow mines. However, with geometric increase demand, contradiction between supply demand becoming more serious, exploitation resources from layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, harsh conditions "three high one disturbance" seriously threaten safety personnel. superposition complex environment makes number input factors sharply, which leads application roadway engineering. guidance not mature, construction various databases missing, there are still some problems universality applicability. To this end, paper starts introduction operating characteristics tools, conducts comprehensive study relevant high-level articles published top journals. It systematically sorts out research progress that successfully solved five directions rock mechanics strength, surrounding stability, rock-burst, roof fall risks micro-seismic events. While objectively evaluating performance different it also expounds its own views on key results. Literature review shows whether development tool or comparative model, ANN than 98%, performs extremely well direction stability risk, accuracy rate 90%. As most mature AI application, mechanical strength experienced process "SVM → DL XGBoost RF". dataset small samples (< 100) big 1000), R2 tree-based models can be stabilized at 95%. rock-burst prediction mainly focuses monitoring data. Whether sample large-scale data BN remains above 85%. evaluation events recent years. image processing CNN important. signal recognition classification accounts for 90%, potential source location needs further explored. In general, nature itself first choice almost all influencing factors. At same time, update iteration methods (micro-seismic, ground sound, separation, deformation, etc.) expands database, making possible obtain due threat life cost equipment, very difficult before. parameter selection method combining lithology conditions, geological will gradually research. Finally, follow-up work collation on-the-spot investigation, existing mines, explores engineering, puts forward focus challenging future, gives opinions.

Язык: Английский

Understanding the Mechanism of Hydraulic Fracturing in Naturally Fractured Carbonate Reservoirs: Microseismic Monitoring and Well Testing DOI
Dmitriy A. Martyushev, Yongfei Yang, Yousef Kazemzadeh

и другие.

Arabian Journal for Science and Engineering, Год журнала: 2023, Номер 49(6), С. 8573 - 8586

Опубликована: Дек. 11, 2023

Язык: Английский

Процитировано

13
Hybrid random forest-based models for predicting shear strength of structural surfaces based on surface morphology parameters and metaheuristic algorithms DOI
Jian Zhou, Peixi Yang, Chuanqi Li

и другие.

Construction and Building Materials, Год журнала: 2023, Номер 409, С. 133911 - 133911

Опубликована: Ноя. 1, 2023

Язык: Английский

Процитировано

11
Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method DOI Creative Commons
Jian Zhou, Yuxin Chen, Hui Chen

и другие.

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Янв. 24, 2023

Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation induced stresses at different positions pillar helpful design guaranteeing stability. There are many modeling methods evaluate their stability, including empirical numerical method. However, difficult be applied places other than original environmental characteristics, often simplify boundary conditions material properties, which cannot guarantee design. Currently, machine learning (ML) algorithms have been successfully assessment with higher accuracy. Thus, study adopted a back-propagation neural network (BPNN) five elements sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization (BOA), tunicate swarm (TSA), multi-verse (MVO). Combining metaheuristic algorithms, hybrid models were developed predict stress within pillar. weight threshold BPNN model optimized by mean absolute error (MAE) utilized as fitness function. A database containing 149 data samples was established, where input variables angle goafline (A), depth working coal seam (H), specific gravity (G), distance point from center (C), (D), output variable stress. Furthermore, predictive performance proposed evaluated metrics, namely coefficient determination (R 2 ), root squared (RMSE), variance accounted (VAF), (MAE), percentage (MAPE). results showed that good prediction performance, especially GWO-BPNN performed best (Training set: R = 0.9991, RMSE 0.1535, VAF 99.91, MAE 0.0884, MAPE 0.6107; Test 0.9983, 0.1783, 99.83, 0.1230, 0.9253).

Язык: Английский

Процитировано

10
Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam DOI
Yanan Qian, Quangui Li, Zhizhong Jiang

и другие.

Gas Science and Engineering, Год журнала: 2024, Номер 122, С. 205222 - 205222

Опубликована: Янв. 18, 2024

Язык: Английский

Процитировано

4
Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review DOI Creative Commons
Bingbing Yu, Bo Wang, Yuantong Zhang

и другие.

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Авг. 19, 2024

The deep integration of computer field and coal mining is the only way to mine intellectualization. A variety artificial intelligence tools have been applied in open-pit shallow mines. However, with geometric increase demand, contradiction between supply demand becoming more serious, exploitation resources from layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, harsh conditions "three high one disturbance" seriously threaten safety personnel. superposition complex environment makes number input factors sharply, which leads application roadway engineering. guidance not mature, construction various databases missing, there are still some problems universality applicability. To this end, paper starts introduction operating characteristics tools, conducts comprehensive study relevant high-level articles published top journals. It systematically sorts out research progress that successfully solved five directions rock mechanics strength, surrounding stability, rock-burst, roof fall risks micro-seismic events. While objectively evaluating performance different it also expounds its own views on key results. Literature review shows whether development tool or comparative model, ANN than 98%, performs extremely well direction stability risk, accuracy rate 90%. As most mature AI application, mechanical strength experienced process "SVM → DL XGBoost RF". dataset small samples (< 100) big 1000), R2 tree-based models can be stabilized at 95%. rock-burst prediction mainly focuses monitoring data. Whether sample large-scale data BN remains above 85%. evaluation events recent years. image processing CNN important. signal recognition classification accounts for 90%, potential source location needs further explored. In general, nature itself first choice almost all influencing factors. At same time, update iteration methods (micro-seismic, ground sound, separation, deformation, etc.) expands database, making possible obtain due threat life cost equipment, very difficult before. parameter selection method combining lithology conditions, geological will gradually research. Finally, follow-up work collation on-the-spot investigation, existing mines, explores engineering, puts forward focus challenging future, gives opinions.

Язык: Английский

Процитировано

4