Assessment of resilient modulus of soil using hybrid extreme gradient boosting models DOI Creative Commons
Xiangfeng Duan

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

Published: Dec. 30, 2024

Accurate estimation of the soil resilient modulus (M

Language: Английский

Improving wettability estimation in carbonate formation using machine learning algorithms: Implications for underground hydrogen storage applications DOI
Grant Charles Mwakipunda,

AL-Wesabi Ibrahim,

Allou Koffi Franck Kouassi

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 111, P. 781 - 797

Published: Feb. 27, 2025

Language: Английский

Citations

2
Prediction of bond strength and failure mode of FRP bars embedded in UHPC or UHPSSC utilising extreme gradient boosting technique DOI
Pei-Fu Zhang, Xiao‐Ling Zhao, Daxu Zhang

et al.

Composite Structures, Journal Year: 2024, Volume and Issue: 346, P. 118437 - 118437

Published: July 31, 2024

Language: Английский

Citations

6
Operational strategy optimization of an existing ground source heat pump (GSHP) system using an XGBoost surrogate model DOI
Chaoran Wang,

Yu Xiong,

Chanjuan Han

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 318, P. 114444 - 114444

Published: June 24, 2024

Language: Английский

Citations

6
Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques DOI Creative Commons
Shahin Alipour Bonab, Yiteng Xing, G.V. Russo

et al.

Superconductor Science and Technology, Journal Year: 2024, Volume and Issue: 37(7), P. 075008 - 075008

Published: May 21, 2024

Abstract The advent of superconducting bulks, due to their compactness and performance, offers new perspectives opportunities in many applications sectors, such as magnetic field shielding, motors/generators, NMR/MRI, bearings, flywheel energy storage, Maglev trains, among others. investigation characterization bulks typically relies on time-consuming expensive experimental campaigns; hence the development effective surrogate models would considerably speed up research progress around them. In this study, we first produced an dataset containing levitation lateral forces between different MgB 2 one permanent magnet under operating conditions. Next, have exploited develop based Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting, Support Vector Regressor (SVR), Kernel Ridge Regression. After tuning hyperparameters AI models, results demonstrated that SVR is superior technique can predict with a worst-case accuracy scenario 99.86% terms goodness fit data. Moreover, response time these for estimation datapoints ultra-fast.

Language: Английский

Citations

5
Prediction of Compressive Strength of Fly Ash-Recycled Mortar Based on Grey Wolf Optimizer–Backpropagation Neural Network DOI Open Access

Shao Jing-jing,

Lin-Bin Li,

Guang-Ji Yin

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(1), P. 139 - 139

Published: Jan. 1, 2025

The evaluation of the mechanical performance fly ash-recycled mortar (FARM) is a necessary condition to ensure efficient utilization recycled fine aggregates. This article describes design nine mix proportions FARMs with low water/cement ratio and screens six reasonable flowability. compressive strengths were tested, influence (w/c) age on strength was analyzed. Meanwhile, backpropagation neural network (BPNN) model optimized by grey wolf optimizer (GWO), namely GWO-BPNN model, established predict FARM. input layer consisted w/c, cement/sand ratio, water reducer, age, ash content, while output strength. data set 150 sets from this existing research in literature, which 70% used for training 30% validation. results show that compared traditional BPNN, coefficient determination (R2) increases 0.85 0.93, mean squared error (MSE) decreases 0.018 0.015. convergence iterations validation decrease 108 65. indicates GWO improved prediction accuracy computational efficiency BPNN. characteristic heat, kernel density estimation, scatter matrix, SHAP value all indicated w/c strongly negatively correlated strength, sand/cement positively However, relationship between contents ash, not obvious.

Language: Английский

Citations

0
Analytical model for describing the dynamic bond-slip behavior between steel plates and reactive powder concrete DOI
Peng Sun, Xiaomeng Hou, Qin Rong

et al.

Structures, Journal Year: 2025, Volume and Issue: 77, P. 109096 - 109096

Published: May 3, 2025

Language: Английский

Citations

0
Service life evaluation of marine concrete structures considering spatial and temporal characteristics: A framework based on multi training-MCS-NLS DOI
Shiqi Wang, Renjie Wu, Fuyuan Gong

et al.

Engineering Structures, Journal Year: 2024, Volume and Issue: 322, P. 119193 - 119193

Published: Oct. 30, 2024

Language: Английский

Citations

3
Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies DOI
Fangyuan Li,

Md. Sohel Rana,

Muhammad Ahmed Qurashi

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Dec. 18, 2024

Language: Английский

Citations

3
Bond strength and failure mode prediction model for recycled aggregate concrete based on intelligent algorithm optimized support vector machine DOI
Congcong Fan, Youliang Ding,

Yuanxun Zheng

et al.

Structures, Journal Year: 2024, Volume and Issue: 71, P. 107999 - 107999

Published: Dec. 20, 2024

Language: Английский

Citations

3
A feature importance-based intelligent method for controlling overbreak in drill-and-blast tunnels via integration with rock mass quality DOI
Yaosheng Liu, Ang Li, Shuaishuai Wang

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 108, P. 1011 - 1031

Published: Sept. 30, 2024

Language: Английский

Citations

1