Low voltage user power internet of things monitoring system based on LoRa wireless technology DOI Creative Commons
Xiaohua Wang, Wei Zhao,

Xixian Niu

et al.

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 27, 2025

Language: Английский

Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India DOI
Subbarayan Saravanan, Devanantham Abijith, Nagireddy Masthan Reddy

et al.

Urban Climate, Journal Year: 2023, Volume and Issue: 49, P. 101503 - 101503

Published: March 18, 2023

Language: Английский

Citations

58
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering DOI Creative Commons
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(21), P. 12655 - 12699

Published: May 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Language: Английский

Citations

21
Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens DOI
Nima Khodadadi, Hossein Roghani, Francisco De Caso

et al.

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: 198, P. 111763 - 111763

Published: March 1, 2024

Language: Английский

Citations

20
Enhancing the performance of gradient boosting trees on regression problems DOI Creative Commons
Lydia Wahid Rizkallah

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 17, 2025

Language: Английский

Citations

2
Prediction model of goaf coal temperature based on PSO-GRU deep neural network DOI Creative Commons
Jun Guo, Changming Chen, Hu Wen

et al.

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 53, P. 103813 - 103813

Published: Nov. 25, 2023

The accurate determination of coal temperature in hidden space such as goaf has always been a worldwide problem that needs to be solved. research prediction model important practical significance for the detection loose temperature. Based on natural ignition experiment and spontaneous combustion gas characterization index initial data set GRU neurons are used mine nonlinear relationship between temperature, parameters optimized by PSO obtain predicted value body results show MAE PSO-GRU is 1.37 °C, 6.51 11.40 15.90 20.20 °C lower than PSO-SVM, PSO-BP, BP, RF SVM models respectively. RMSE decreased 0.45 4.44 10.33 15.71 24.24 judgment coefficient R2 test training 0.99, generalization, accuracy robustness all good. experimental inversion error within range 3.87 %, maximum difference 5.69 average 2.83 which can meet requirements field measurement.

Language: Английский

Citations

24
Research on hybrid strategy Particle Swarm Optimization algorithm and its applications DOI Creative Commons

Jicheng Yao,

Xiaonan Luo, Fang Li

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 22, 2024

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development advanced algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, Hook-Jeeves strategy to enhance both global search capabilities. HSPSO is evaluated using CEC-2005 CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns (HBF-PSO), Butterfly Algorithm (BOA), Ant Colony (ACO), Firefly (FA). Experimental results show that achieves optimal terms best fitness, average stability. Additionally, applied feature selection for UCI Arrhythmia dataset, resulting high-accuracy classification model outperforms traditional methods. These findings establish an effective solution

Language: Английский

Citations

12
Residual stress prediction in laser shock peening induced LD-TC4 alloy by data-driven ensemble learning methods DOI
Butong Li,

Junjie Zhu,

Xufeng Zhao

et al.

Optics & Laser Technology, Journal Year: 2024, Volume and Issue: 176, P. 110946 - 110946

Published: April 8, 2024

Language: Английский

Citations

9
Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames DOI
Ahmet Demir, Emrehan Kutluğ Şahin, Selçuk Demir

et al.

Structures, Journal Year: 2024, Volume and Issue: 64, P. 106524 - 106524

Published: May 11, 2024

Language: Английский

Citations

9
A novel tool for probabilistic modeling of liquefaction behavior in alluvial soil DOI
Sufyan Ghani,

Sunita Kumari

Georisk Assessment and Management of Risk for Engineered Systems and Geohazards, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: Aug. 27, 2024

This research introduces and validates advanced machine learning models designed to predict the probability of liquefaction failure (pf) in alluvial soil deposits. Three optimisation algorithms namely Northern Goshawk Optimization (NGO), Jellyfish Search Optimizer (JSO), Horse Herd Algorithm (HHO) coupled with Adaptive Neuro Fuzzy inference system (AFS) has been employed present research. Among tested, AFS-HHO model exhibited superior predictive ability, R2 = 0.93 RMSE 0.06 during stage, 0.89 0.07 testing stage. highlights model's efficiency accurately predicting pf using only corrected SPT-N value i.e. (N1)60 cyclic stress ratio (CSR). The study also emphasises importance influencing probabilistic assessment failure, proposes a novel chart as reliable tool for estimating Considering overall analysis, proposed offer geotechnical engineers estimate thereby holding substantial implications field evaluation studies.

Language: Английский

Citations

9
Prediction method for the dynamic response of expressway lateritic soil subgrades on the basis of Bayesian optimization CatBoost DOI
Xuanjia Huang, Weizheng Liu, Qing Guo

et al.

Soil Dynamics and Earthquake Engineering, Journal Year: 2024, Volume and Issue: 186, P. 108943 - 108943

Published: Sept. 5, 2024

Language: Английский

Citations

9