State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Artificial Intelligence Review, Год журнала: 2024, Номер 57(1)

Опубликована: Янв. 1, 2024

Язык: Английский

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

Transportation Geotechnics, Год журнала: 2022, Номер 36, С. 100819 - 100819

Опубликована: Июль 21, 2022

Язык: 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, Год журнала: 2022, Номер 36, С. 100806 - 100806

Опубликована: Июль 8, 2022

Язык: Английский

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Application of Six Metaheuristic Optimization Algorithms and Random Forest in the uniaxial compressive strength of rock prediction

Applied Soft Computing, Год журнала: 2022, Номер 131, С. 109729 - 109729

Опубликована: Окт. 20, 2022

Язык: Английский

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

Mining Metallurgy & Exploration, Год журнала: 2023, Номер unknown

Опубликована: Фев. 3, 2023

Язык: Английский

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Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

Applied Soft Computing, Год журнала: 2023, Номер 147, С. 110808 - 110808

Опубликована: Сен. 4, 2023

Язык: Английский

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A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Artificial Intelligence Review, Год журнала: 2024, Номер 57(6)

Опубликована: Май 15, 2024

Abstract In recent years, swarm intelligence optimization algorithms have been proven to significant effects in solving combinatorial problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into form novel has proposed new direction for better The longhorn beetle whisker search algorithm an emerging heuristic algorithm, originates from simulation foraging behavior. This simulates touch strategy required by beetles during foraging, and achieves efficient complex problem spaces through bioheuristic methods. article reviews progress on 2017 present. Firstly, basic principle model structure were introduced, its differences connections with other analyzed. Secondly, this paper summarizes achievements scholars years improvement algorithms. Then, application various fields was explored, including function optimization, engineering design, path planning. Finally, proposes future directions, deep learning fusion, processing multimodal problems, etc. Through review, readers will comprehensive understanding status prospects providing useful guidance practical

Язык: Английский

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Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties

Nondestructive Testing And Evaluation, Год журнала: 2024, Номер unknown, С. 1 - 24

Опубликована: Фев. 5, 2024

The dynamic compressive strength (DCS) of frozen-thawed rock influences the stability mass in cold regions, especially when masses are possibly disturbed by loads. Laboratory freeze-thaw weathering treatment is usually time-consuming, and test destructive. Therefore, this paper attempts to quickly predict DCS sandstones using data-driven methods, non-destructive properties, basic environmental parameters. sparrow search algorithm (SSA), gorilla troops optimiser, dung beetle optimiser were chosen develop two hyperparameters random forest (RF). classic RF, back propagation neural network, support vector regression models taken as control group. These six developed DCS. Their prediction results compared. Finally, sensitivity analysis was carried out assess significance all input variables. indicate that SSA – RF model yields best result, three optimised have better performance than single machine-learning models. Strain rate, dry density, wave velocity found be most important parameters prediction, which further indicates there also a strong correlation between characteristic impedance

Язык: Английский

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A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock

Applied Sciences, Год журнала: 2022, Номер 12(17), С. 8468 - 8468

Опубликована: Авг. 24, 2022

Uniaxial compressive strength (UCS) is one of the most important parameters to characterize rock mass in geotechnical engineering design and construction. In this study, a novel kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model was proposed predict UCS 271 samples. Four namely porosity (Pn, %), Schmidt hardness rebound number (SHR), P-wave velocity (Vp, km/s), point load (PLS, MPa) were considered as input variables, output variable. To verify effectiveness accuracy KELM-GWO model, machine (ELM), KELM, deep (DELM) back-propagation neural network (BPNN), empirical established compared with UCS. The root mean square error (RMSE), determination coefficient (R2), absolute (MAE), prediction (U1), quality (U2), variance accounted for (VAF) adopted evaluate all models study. results demonstrate that best predicting performance indices. Additionally, identified parameter by using impact value (MIV) technique.

Язык: 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, Год журнала: 2022, Номер 18(3), С. 1431 - 1446

Опубликована: Сен. 2, 2022

Язык: Английский

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