Journal of Computational Science, Год журнала: 2024, Номер 78, С. 102266 - 102266
Опубликована: Март 15, 2024
Язык: Английский
Journal of Computational Science, Год журнала: 2024, Номер 78, С. 102266 - 102266
Опубликована: Март 15, 2024
Язык: Английский
Artificial Intelligence Review, Год журнала: 2024, Номер 57(1)
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
23Transportation Geotechnics, Год журнала: 2022, Номер 36, С. 100819 - 100819
Опубликована: Июль 21, 2022
Язык: Английский
Процитировано
63Transportation Geotechnics, Год журнала: 2022, Номер 36, С. 100806 - 100806
Опубликована: Июль 8, 2022
Язык: Английский
Процитировано
42Applied Soft Computing, Год журнала: 2022, Номер 131, С. 109729 - 109729
Опубликована: Окт. 20, 2022
Язык: Английский
Процитировано
42Mining Metallurgy & Exploration, Год журнала: 2023, Номер unknown
Опубликована: Фев. 3, 2023
Язык: Английский
Процитировано
32Applied Soft Computing, Год журнала: 2023, Номер 147, С. 110808 - 110808
Опубликована: Сен. 4, 2023
Язык: Английский
Процитировано
25Artificial 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
Процитировано
18Nondestructive 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
Процитировано
12Applied 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
Процитировано
36Acta Geotechnica, Год журнала: 2022, Номер 18(3), С. 1431 - 1446
Опубликована: Сен. 2, 2022
Язык: Английский
Процитировано
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