A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters DOI
Jian Zhou, Peixi Yang,

Weixun Yong

et al.

Acta Geophysica, Journal Year: 2024, Volume and Issue: 72(3), P. 1847 - 1866

Published: April 15, 2024

Language: Английский

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization DOI

Rana Muhammad Adnan,

Reham R. Mostafa, Özgür Kişi

et al.

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 230, P. 107379 - 107379

Published: Aug. 12, 2021

Language: Английский

Citations

169
Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the perspective of compressive strength and splitting tensile strength DOI
S. Y. Zhang, Wenguang Chen, Jinjun Xu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109170 - 109170

Published: Aug. 27, 2024

Language: Английский

Citations

30
Long-term safety evaluation of soft rock tunnel structure based on knowledge decision-making and data-driven models DOI
Liangliang Zhao, Wenbo Yang, Zhilong Wang

et al.

Computers and Geotechnics, Journal Year: 2024, Volume and Issue: 169, P. 106244 - 106244

Published: March 20, 2024

Language: Английский

Citations

17
Prediction of tunnel deformation using PSO variant integrated with XGBoost and its TBM jamming application DOI
Yin Bo, Xiaogang Guo,

Quansheng Liu

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 150, P. 105842 - 105842

Published: May 24, 2024

Language: Английский

Citations

17
Forecasting the Green Behaviour Level of Chinese Enterprises: A Conjoined Application of the Autoregressive Integrated Moving Average (ARIMA) Model and Multi-Scenario Simulation DOI
Liping Wang, Longjun Chen,

Shucen Jin

et al.

Technology in Society, Journal Year: 2025, Volume and Issue: unknown, P. 102825 - 102825

Published: Jan. 1, 2025

Language: Английский

Citations

3
Cumulative Major Advances in Particle Swarm Optimization from 2018 to the Present: Variants, Analysis and Applications DOI
Donglin Zhu, R R Li, Yangyang Zheng

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

2
Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes DOI Creative Commons
Hemn Unis Ahmed, Ahmed Salih Mohammed, Azad A. Mohammed

et al.

PLoS ONE, Journal Year: 2021, Volume and Issue: 16(6), P. e0253006 - e0253006

Published: June 14, 2021

Geopolymer concrete is an inorganic that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to geopolymer being eco-efficient and environmentally friendly construction material. A variety used in such fly ash, ground granulated blast furnace slag, rice husk metakaolin Palm oil fuel ash was commonly consumed prepare composites. The most important mechanical property for all types composites, including concrete, compressive strength. However, structural design field, strength at 28 days essential. Therefore, achieving authoritative model predicting necessary regarding saving time, energy, cost-effectiveness. It gives guidance scheduling process removal formworks. In study, Linear (LR), Non-Linear (NLR), Multi-logistic (MLR) regression models were develop predictive estimating ash-based (FA-GPC). regard, a comprehensive dataset consists 510 samples collected several academic research studies analyzed models. modeling process, first twelve effective variable parameters on FA-GPC, SiO2/Al2O3 (Si/Al) binder, alkaline liquid ratio (l/b), (FA) content, fine aggregate (F) coarse (C) sodium hydroxide (SH)content, silicate (SS) (SS/SH), molarity (M), curing temperature (T), duration inside ovens (CD) specimen ages (A) considered input parameters. Various statistical assessments Root Mean Squared Error (RMSE), Absolute (MAE), Scatter Index (SI), OBJ value, Coefficient determination (R2) evaluate efficiency developed results indicated NLR performed better FA-GPC mixtures compared other Moreover, sensitivity analysis demonstrated temperature, ratio, content are affecting parameter FA-GPC.

Language: Английский

Citations

87
The spatiotemporal evolution pattern of urban resilience in the Yangtze River Delta urban agglomeration based on TOPSIS-PSO-ELM DOI
Chenhong Xia,

Guofang Zhai

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 87, P. 104223 - 104223

Published: Oct. 2, 2022

Language: Английский

Citations

62
Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning DOI
Xin Yin, Quansheng Liu, Xing Huang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2021, Volume and Issue: 120, P. 104285 - 104285

Published: Dec. 2, 2021

Language: Английский

Citations

58
Multi-objective optimization control for tunnel boring machine performance improvement under uncertainty DOI
Wenli Liu, Ang Li, Congjian Liu

et al.

Automation in Construction, Journal Year: 2022, Volume and Issue: 139, P. 104310 - 104310

Published: May 7, 2022

Language: Английский

Citations

57