Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration DOI Creative Commons
Shugang Zhao, Liguan Wang,

Mingyu Cao

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(9), С. 3759 - 3759

Опубликована: Апрель 28, 2024

In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN is compared with other established methods, including swarm (PSO-ANN), genetic algorithm-artificial (GA-ANN), single ANN, and USBM empirical model. aim to demonstrate superiority of PPV prediction. Utilizing dataset comprising 180 blasting events from Tonglushan Copper Mine in China, investigated performance each results showed that outperforms models terms prediction accuracy robustness. This study highlights effectiveness promising tool mining operations, contributing safer more efficient practices.

Язык: Английский

Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting DOI
Xiaohua Ding, Mahdi Hasanipanah, Dmitrii Vladimirovich Ulrikh

и другие.

Natural Resources Research, Год журнала: 2024, Номер 33(3), С. 1349 - 1363

Опубликована: Март 11, 2024

Язык: Английский

Процитировано

11

Estimating Brazilian Tensile Strength of Granite Rocks Using Metaheuristic Algorithms-Based Self-Organizing Neural Networks DOI

Ziguang He,

Shane B. Wilson,

Masoud Monjezi

и другие.

Rock Mechanics and Rock Engineering, Год журнала: 2024, Номер 57(7), С. 4653 - 4668

Опубликована: Март 13, 2024

Язык: Английский

Процитировано

3

Prediction of peak particle vibration velocity based on intelligent optimization algorithm combined with XGBoost DOI
Feng Gao,

Jinxi Xie,

Xin Xiong

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127654 - 127654

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Prediction of blast vibration velocity based on multi-model dynamic weighting ensemble DOI

Weisu Weng,

M. Zhang, Yan Zhao

и другие.

Mechanics of Advanced Materials and Structures, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Апрель 27, 2025

Язык: Английский

Процитировано

0

Advancing the Prediction and Evaluation of Blast-Induced Ground Vibration Using Deep Ensemble Learning with Uncertainty Assessment DOI Creative Commons
Sinem Bozkurt Keser, Mahmut Yavuz, Gamze Erdogan Erten

и другие.

Geosciences, Год журнала: 2025, Номер 15(5), С. 182 - 182

Опубликована: Май 19, 2025

Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling blast-induced ground are imperative for protection sustainable development. The empirical approaches give inaccurate results, as evident literature. Hence, numerous researchers have started to use fast-growing soft computing that satisfying performance. However, achieving high-prediction performance detecting uncertainty crucial, especially operations. This study aims propose a deep ensemble model predict quantify uncertainty, which usually not addressed. used 200 published data from ten granite quarry sites Ibadan Abeokuta areas, Nigeria. equation (United States Bureau Mines-based approach) was applied comparison. comparison models demonstrated proposed achieved superior performance, offering more predictions reliable quantification. Specifically, it exhibited lowest root mean square error (22.674), negative log-likelihood (4.44), interval width (1.769), alongside highest R2 value (0.77) coverage probability (0.95). reached desired 95%, demonstrating underestimated or overestimated.

Язык: Английский

Процитировано

0

A comprehensive survey on machine learning applications for drilling and blasting in surface mining DOI Creative Commons
V. S. K. R. Munagala, Srikanth Thudumu, Irini Logothetis

и другие.

Machine Learning with Applications, Год журнала: 2023, Номер 15, С. 100517 - 100517

Опубликована: Дек. 11, 2023

Drilling and blasting operations are pivotal for productivity safety in hard rock surface mining. These restricted due to complexities such as site-specific uncertainties, risks, environmental economic constraints. Machine Learning (ML) is a transformative approach tackle these resulting significant cost reductions. ML applications can reduce overall costs by up 23% decrease the amount of explosives much 89% compared traditional methods. This survey presents comprehensive review how be applied optimize drill blast designs while accounting its operational challenges. Our research highlights difficulties collecting quality data, complexity interpreting this data into insightful information, selection models relating mining objectives, need established methods assess efficiency quantitatively. We provide synthesis model development practices drilling demonstrate value methodologies. Based on our survey, we present actionable recommendations developing methodologies improve safety, costs, enhance processes. includes establishing standardized schematics, multiobjective optimization, evaluation metrics. benefits guide mine management engineers adopt techniques on-ground practices. aims serve resource both practitioners researchers shaping future direction

Язык: Английский

Процитировано

6

Predicting the minimum horizontal principal stress using genetic expression programming and borehole breakout data DOI Creative Commons
Rui Zhang, Jian Zhou

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

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

2

Prediction of peak particle velocity using hybrid random forest approach DOI Creative Commons
Yu Yan, Jiwei Guo,

Shijie Bao

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 28, 2024

Blasting excavation is widely used in mining, tunneling and construction industries, but it leads to produce ground vibration which can seriously damage the urban communities. The peak particle velocity (PPV) one of main indicators for determining extent vibration. Owing complexity blasting process, there controversy over parameters will be considered as inputs empirical equations machine learning (ML) algorithms. According current researches, burden has controversial impact on blast-induced To judge whether affects vibration, data considering have been recorded at Wujiata coal mine. Correlation coefficient analyze relationship between variables, correlation distance from center monitored point (R) greatest value - 0.67. This study firstly summarizes most common equations, a new equation established by dimension analysis. shows better performance predicting PPV than other regression Secondly, confirmed applicability PPV. Based assessments, error characteristic curve Uncertainty analysis first round PPV, random forest (RF) K-Nearest Neighbors (KNN) show four Then, second round, based artithmetic optimization algorithm (AOA), optimized (AOA-RF) model accurate compared with (AOA-KNN) presented literature. Finally, points predicted informed danger are marked Chinese safety regulations blasting.

Язык: Английский

Процитировано

2

Measurement and Prediction of Blast-Induced Flyrock Distance Using Unmanned Aerial Vehicles and Metaheuristic-Optimized ANFIS Neural Networks DOI
Hoang Nguyen,

Nguyen Van Thieu

Natural Resources Research, Год журнала: 2024, Номер unknown

Опубликована: Дек. 21, 2024

Язык: Английский

Процитировано

1

Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration DOI Creative Commons
Shugang Zhao, Liguan Wang,

Mingyu Cao

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(9), С. 3759 - 3759

Опубликована: Апрель 28, 2024

In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN is compared with other established methods, including swarm (PSO-ANN), genetic algorithm-artificial (GA-ANN), single ANN, and USBM empirical model. aim to demonstrate superiority of PPV prediction. Utilizing dataset comprising 180 blasting events from Tonglushan Copper Mine in China, investigated performance each results showed that outperforms models terms prediction accuracy robustness. This study highlights effectiveness promising tool mining operations, contributing safer more efficient practices.

Язык: Английский

Процитировано

0