Novel model for risk identification during karst excavation DOI
Song-Shun Lin, Shui‐Long Shen, Annan Zhou

et al.

Reliability Engineering & System Safety, Journal Year: 2021, Volume and Issue: 209, P. 107435 - 107435

Published: Jan. 9, 2021

Language: Английский

Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements DOI
Kun Zhang, Hai‐Min Lyu, Shui‐Long Shen

et al.

Tunnelling and Underground Space Technology, Journal Year: 2020, Volume and Issue: 106, P. 103594 - 103594

Published: Oct. 23, 2020

Language: Английский

Citations

112
Sustainable development and environmental restoration in Lake Erhai, China DOI
Song-Shun Lin, Shui‐Long Shen, Annan Zhou

et al.

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 258, P. 120758 - 120758

Published: Feb. 27, 2020

Language: Английский

Citations

91
The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity DOI
Yonggang Zhang, Yuanlun Xie, Yan Zhang

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2021, Volume and Issue: 80(6), P. 5053 - 5060

Published: April 22, 2021

Language: Английский

Citations

90
Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO DOI
Navid Kardani, Abidhan Bardhan, Dookie Kim

et al.

Journal of Building Engineering, Journal Year: 2020, Volume and Issue: 35, P. 102105 - 102105

Published: Dec. 19, 2020

Language: Английский

Citations

89
Determinants of Learning Management Systems during COVID-19 Pandemic for Sustainable Education DOI Open Access
Nadire Çavuş, Yakubu Bala Mohammed, Mohammed Nasiru Yakubu

et al.

Sustainability, Journal Year: 2021, Volume and Issue: 13(9), P. 5189 - 5189

Published: May 6, 2021

Research has shown that effective and efficient learning management systems (LMS) were the main reasons for sustainable education in developed nations during COVID-19 pandemic. However, due to slow take-up of LMS many schools developing countries, especially Africa completely shut down To fill this gap, 4 AI-based models; Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Boosted Tree (BRT) prediction determinants. Nonlinear sensitivity analysis was employed select key parameters determinants data obtained from 1244 schools’ students. Five statistical indices used validate models. The performance results four AI models discovered facilitating conditions, attitude towards LMS, perceived enjoyment, users’ satisfaction, usefulness, ease use be most significant factors affect educational sustainability Nigeria COVID-19. Further, single model’s comparison proved SVM highest ability compared GPR, ANN, BRT its robustness handling uncertainties. study identified responsible total closure Future studies should examine application other linear nonlinear techniques.

Language: Английский

Citations

85
Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms DOI Creative Commons
Song-Shun Lin, Shui‐Long Shen, Ning Zhang

et al.

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(5), P. 101177 - 101177

Published: Feb. 23, 2021

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance (EPB) shield tunnelling. Five artificial intelligence (AI) models based on machine and deep learning techniques—back-propagation neural network (BPNN), extreme (ELM), support vector (SVM), long-short term memory (LSTM), gated recurrent unit (GRU)—are used. geological nine operational parameters that influence are considered. A field case of tunnelling in Shenzhen City, China is analyzed using developed models. total 1000 datasets adopted to establish The prediction performance five ranked as GRU > LSTM SVM ELM BPNN. Moreover, Pearson correlation coefficient (PCC) sensitivity analysis. results reveal main thrust (MT), penetration (P), foam volume (FV), grouting (GV) have strong correlations with (AS). An empirical formula constructed high-correlation influential factors their corresponding datasets. Finally, performances method compared. all perform better than method.

Language: Английский

Citations

80
Modelling of municipal solid waste gasification using an optimised ensemble soft computing model DOI
Navid Kardani, Annan Zhou, Majidreza Nazem

et al.

Fuel, Journal Year: 2020, Volume and Issue: 289, P. 119903 - 119903

Published: Dec. 19, 2020

Language: Английский

Citations

79
Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method DOI
Ning Zhang, Annan Zhou, Yutao Pan

et al.

Measurement, Journal Year: 2021, Volume and Issue: 183, P. 109700 - 109700

Published: June 23, 2021

Language: Английский

Citations

78
Excess pore water pressure caused by the installation of jet grouting columns in clay DOI

Zhi-Feng Wang,

Shui‐Long Shen, Giuseppe Modoni

et al.

Computers and Geotechnics, Journal Year: 2020, Volume and Issue: 125, P. 103667 - 103667

Published: June 7, 2020

Language: Английский

Citations

75
Distribution characteristics and utilization of shallow geothermal energy in China DOI
Ye‐Shuang Xu, Xuwei Wang, Shui‐Long Shen

et al.

Energy and Buildings, Journal Year: 2020, Volume and Issue: 229, P. 110479 - 110479

Published: Sept. 15, 2020

Language: Английский

Citations

71