Research on the attenuation characteristics of seismic energy in multicoal seam mining and the warning method of rock burst DOI Creative Commons
Hongwei Mu, Yongliang Zhang, Mingzhong Gao

и другие.

Energy Science & Engineering, Год журнала: 2024, Номер 12(11), С. 4932 - 4949

Опубликована: Ноя. 1, 2024

Abstract The mechanism of rock burst induced by the superposition dynamic and static loads in multicoal seam mining is unique. To investigate propagation attenuation law large‐energy microseismic events under this condition, study employs FLAC3D's module to simulate analyze influence distance, overburden structure mining, interlayer plastic zone on vibration wave attenuation. Results indicate that when coal seams are mined at close distances, waves experience significant while passing through between two layers coal. At equal structures exhibit greater effects Considering differences rock‐burst induction mechanisms close‐distance group versus single a discriminant criterion for bursts superimposed established along with monitoring early warning method suitable such conditions.

Язык: Английский

Study on Intelligent Classing of Public Welfare Forestland in Kunyu City DOI Creative Commons
Meng Sha,

Hua Yang,

Jian Wu

и другие.

Land, Год журнала: 2025, Номер 14(1), С. 89 - 89

Опубликована: Янв. 5, 2025

Manual forestland classification methods, which rely on predetermined scoring criteria and subjective interpretation, are commonly used but suffer from limitations such as high labor costs, complexity, lack of scalability. This study proposes an innovative machine learning-based approach to classification, utilizing a Support Vector Machine (SVM) model automate the process enhance both efficiency accuracy. The main contributions this work follows: A learning was developed using integrated data Third National Land Survey China, including forestry, grassland, wetland datasets. Unlike previous approaches, SVM is optimized with Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) automatically determine parameters, overcoming manual rule-based methods. performance evaluated confusion matrices, accuracy, Matthews Correlation Coefficient (MCC). comprehensive comparison under different optimization techniques revealed significant improvements in accuracy generalization ability over systems. experimental results demonstrated that GA-SVM achieved accuracies 98.83% (test set) 99.65% (overall sample), MCC values 0.9796 0.990, respectively, outpacing other algorithms, (GS) (PSO). applied classify public welfare Kunyu City, yielding detailed classifications across various categories. result provides more efficient accurate method for large-scale management, implications future land use assessments. findings underscore advantages classification: it efficient, accurate, easy operate. not only presents reliable alternative conventional methods also sets precedent optimize applications.

Язык: Английский

Процитировано

0

Prediction of Coal Burst Location and Risk Level in Roadway Using XGBoost with Multi-element Microseismic Information and Its Application in Steeply Inclined Ultra-Thick Coal Seam DOI
Feng Cui, Chengqing Zong, Xiongming Lai

и другие.

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

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0

Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM DOI Creative Commons

Linyu Li,

You Wenbin,

Yonghong Ding

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(3), С. 1343 - 1343

Опубликована: Янв. 27, 2025

The challenge in reusing high-impact recorders lies developing an efficient and accurate failure prediction model under small-sample conditions. To address this issue, study proposes IPSO-SVM model. First, the particle swarms IPSO algorithm were grouped based on their exploration exploitation functions, dynamic inertia weight mechanisms designed accordingly. grouping ratio was dynamically adjusted during iterations to enhance optimization performance. Tests using benchmark functions verified that approach improves convergence accuracy stability compared conventional PSO algorithms. Subsequently, 5-fold cross-validation of SVM used as fitness value, employed optimize penalty kernel parameters Trained experimental data, achieved a 90.5%, outperforming PSO-SVM model’s 85%. These results demonstrate potential addressing challenges

Язык: Английский

Процитировано

0

Case study on pre-warning and protective measures against rockbursts utilizing the microseismic method in deep underground mining DOI
Longjun Dong, Xianhang Yan, Jiachuang Wang

и другие.

Journal of Applied Geophysics, Год журнала: 2025, Номер unknown, С. 105687 - 105687

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Residential building cooling load prediction with optimized KELM models and interpretability insights DOI
Yulin Zhang, Enming Li, Jianan Gu

и другие.

Applied Thermal Engineering, Год журнала: 2025, Номер unknown, С. 126421 - 126421

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

A Methodology for Situation Assessing of Space-Based Information Networks DOI Creative Commons

Sai Xu,

Jun Liu,

JIA-WEI TANG

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4127 - 4127

Опубликована: Апрель 9, 2025

This paper proposes a cloud-edge collaborative method for operational situation assessment to ensure the efficient and reliable operation of space-based information networks. By analyzing time-varying network topology characteristics, we establish 14-dimensional factor system that can characterize Considering resource constraints satellites, traditional on-orbit methods often lead high latency excessive consumption. A is introduced enhance efficiency. The proposed first applies principal component analysis dimensionality reduction, followed by pre-labeling situational data using an improved K-means clustering algorithm. individual satellites then performed particle swarm optimization-support vector machine Finally, fusion networks conducted at ground cloud center, incorporating weighting factors. Experimental results demonstrate improves accuracy 13% compared baseline methods, significantly reduces average completion time, maintains stable performance in large-scale satellite constellations.

Язык: Английский

Процитировано

0

A novel cluster-based ensemble learning method for long-term rockburst risk prediction and its application DOI
Leilei Liu, Weizhang Liang, Guoyan Zhao

и другие.

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 162, С. 106678 - 106678

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0

An intelligent early warning method for tunnel surrounding rock instability under seepage conditions using particle swarm optimization and support vector machine DOI
Chao Jia,

Shuai Cheng,

Liping Li

и другие.

Physics of Fluids, Год журнала: 2025, Номер 37(5)

Опубликована: Май 1, 2025

Geological disasters such as instability of surrounding rock are prone to occur in seepage environment during tunnel construction, which will not only affect the construction progress but also seriously threaten lives workers. Establishing intelligent early warning methods for disaster risks is great significance safe engineering. This paper proposes an indicator system rocks that covers geological information, geophysical drilling and physical-field monitoring information face. Second, model proposed based on PSO (particle swarm optimization algorithm)-SVM (support vector machine algorithm), realizes accurate environment. Third, developed. method successfully applied Haidong Tunnel Dali Section II Dianzhong Water-Diversion Project, proving effectiveness practicality method.

Язык: Английский

Процитировано

0

Predicting short-term rockburst intensity using a weighted probability stacking model with optimal feature selection and Bayesian hidden layer DOI
Jiahao Sun, Wenjie Wang,

Lianku Xie

и другие.

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 153, С. 106021 - 106021

Опубликована: Авг. 20, 2024

Язык: Английский

Процитировано

3

Recognition of Drill String Vibration State Based on WGAN-div and CNN-IWPSO-SVM DOI

Fengtao Qu,

Hualin Liao,

Ming Lu

и другие.

Geoenergy Science and Engineering, Год журнала: 2024, Номер unknown, С. 213342 - 213342

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

3