Success and challenges in predicting TBM penetration rate using recurrent neural networks

Tunnelling and Underground Space Technology, Год журнала: 2022, Номер 130, С. 104728 - 104728

Опубликована: Сен. 1, 2022

Язык: Английский

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Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing

Environmental Earth Sciences, Год журнала: 2022, Номер 81(18)

Опубликована: Сен. 1, 2022

Язык: Английский

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Accurate prediction of concrete compressive strength based on explainable features using deep learning

Construction and Building Materials, Год журнала: 2022, Номер 329, С. 127082 - 127082

Опубликована: Март 19, 2022

Язык: Английский

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Using artificial neural networks for predicting mechanical and radiation shielding properties of different nano-concretes exposed to elevated temperature

Construction and Building Materials, Год журнала: 2022, Номер 324, С. 126663 - 126663

Опубликована: Фев. 3, 2022

Язык: Английский

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Prediction of axial compressive capacity of CFRP-confined concrete-filled steel tubular short columns based on XGBoost algorithm

Engineering Structures, Год журнала: 2022, Номер 260, С. 114239 - 114239

Опубликована: Апрель 21, 2022

Язык: Английский

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XGBoost algorithm-based prediction of safety assessment for pipelines

International Journal of Pressure Vessels and Piping, Год журнала: 2022, Номер 197, С. 104655 - 104655

Опубликована: Март 25, 2022

Язык: Английский

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Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading

Engineering Structures, Год журнала: 2022, Номер 276, С. 115392 - 115392

Опубликована: Дек. 12, 2022

Язык: Английский

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Prediction of Interface Bond Strength Between Ultra-High-Performance Concrete (UHPC) and Normal Strength Concrete (NSC) Using a Machine Learning Approach

Arabian Journal for Science and Engineering, Год журнала: 2022, Номер 47(4), С. 5337 - 5363

Опубликована: Янв. 16, 2022

Язык: Английский

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Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Archives of Computational Methods in Engineering, Год журнала: 2022, Номер 30(3), С. 1979 - 2012

Опубликована: Дек. 20, 2022

Abstract The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. key emphasis was placed on variants of swarm intelligence algorithms, artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, ant colony, firefly fish Krill herd because they had been widely employed in sector. There a broad consensus among scholars that certain BIAs' were more effective than others. For example, Ant Colony Optimization Algorithm best suited farm machinery path optimization pest detection, other applications. On contrary, useful determining plant evapotranspiration rates, which predicted water requirements irrigation process. Despite promising adoption hyper-heuristic agriculture remained low. No universal could perform multiple functions farms; different designed to specific functions. Secondary concerns relate data integrity cyber security, considering history cyber-attacks smart farms. concerns, benefits associated with BIAs outweighed risks. average, farmers can save 647–1866 L fuel is equivalent US$734-851, use GPS-guided systems. accuracy mitigated risk errors applying pesticides, fertilizers, irrigation, crop monitoring better yields.

Язык: Английский

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Prediction of the load-shortening curve of CFST columns using ANN-based models

Journal of Building Engineering, Год журнала: 2022, Номер 51, С. 104279 - 104279

Опубликована: Фев. 26, 2022

Язык: Английский

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