Prediction of Uniaxial Strength of Rocks Using Relevance Vector Machine Improved with Dual Kernels and Metaheuristic Algorithms DOI
Jitendra Khatti, Kamaldeep Singh Grover

Rock Mechanics and Rock Engineering, Год журнала: 2024, Номер 57(8), С. 6227 - 6258

Опубликована: Апрель 1, 2024

Язык: Английский

Revealing the nature of soil liquefaction using machine learning DOI Creative Commons
Sufyan Ghani, Ishwor Thapa,

Amrendra Kumar

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

2
Accurate prediction of concrete compressive strength based on explainable features using deep learning DOI
Ziyue Zeng, Zheyu Zhu, Wu Yao

и другие.

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

Опубликована: Март 19, 2022

Язык: Английский

Процитировано

59
Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering DOI Creative Commons
Chrysanthos Maraveas, Panagiotis G. Asteris, Konstantinos G. Arvanitis

и другие.

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

Процитировано

43
Concrete compressive strength prediction using an explainable boosting machine model DOI Creative Commons
Gaoyang Liu, Bochao Sun

Case Studies in Construction Materials, Год журнала: 2023, Номер 18, С. e01845 - e01845

Опубликована: Янв. 11, 2023

The mixing ratio of the raw materials has a significant impact on concrete compressive strength. Although strength can be inferred from mix ratio, it is frequently challenging to determine how each parameter affects results. In this study, an Explainable Boosting Machine (EBM) applied predict and explain contribution factors Meanwhile, combined algorithm selection hyperparameter optimization problem in machine learning implemented by employing Bayesian technique. A dataset consisting 1030 test data been used for model building. results show that iteratively constructs algorithmic/hyperparametric identifies optimal point space, significantly reducing time consumption ML building process. terms prediction performance, EBM shows excellent with R2 = 0.93, RMSE 4.33, MAE 3.10. allows one fully interpret individual features both global local aspects, which compression further determined.

Язык: Английский

Процитировано

40
Predicting the slump of industrially produced concrete using machine learning: A multiclass classification approach DOI
Xueqing Zhang, Muhammad Zeshan Akber, Wei Zheng

и другие.

Journal of Building Engineering, Год журнала: 2022, Номер 58, С. 104997 - 104997

Опубликована: Июль 31, 2022

Язык: Английский

Процитировано

39
Estimation of Settlement of Pile Group in Clay Using Soft Computing Techniques DOI
Jitendra Khatti, Hanan Samadi, Kamaldeep Singh Grover

и другие.

Geotechnical and Geological Engineering, Год журнала: 2023, Номер 42(3), С. 1729 - 1760

Опубликована: Сен. 18, 2023

Язык: Английский

Процитировано

38
Estimation of rubberized concrete frost resistance using machine learning techniques DOI
Xifeng Gao, Jian Yang, Han Zhu

и другие.

Construction and Building Materials, Год журнала: 2023, Номер 371, С. 130778 - 130778

Опубликована: Фев. 24, 2023

Язык: Английский

Процитировано

35
Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters DOI
Navaratnarajah Sathiparan, Pratheeba Jeyananthan

Materials Today Communications, Год журнала: 2023, Номер 35, С. 106282 - 106282

Опубликована: Май 26, 2023

Язык: Английский

Процитировано

35
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC DOI
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly

и другие.

Structures, Год журнала: 2023, Номер 57, С. 105062 - 105062

Опубликована: Авг. 18, 2023

Язык: Английский

Процитировано

31
Machine learning models for predicting concrete beams shear strength externally bonded with FRP DOI
Jesika Rahman,

Palisa Arafin,

A. H. M. Muntasir Billah

и другие.

Structures, Год журнала: 2023, Номер 53, С. 514 - 536

Опубликована: Апрель 29, 2023

Язык: Английский

Процитировано

27