Cited by Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images

Predicting Energy Consumption of Residential Buildings Using Metaheuristic-optimized Artificial Neural Network Technique in Early Design Stage DOI

Mosbeh R. Kaloop, Furquan Ahmad,

Pijush Samui

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112749 - 112749

Published: Feb. 1, 2025

Language: Английский

Citations

Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses DOI

Pin Zhang, Heng Li, Q. P. Ha

et al.

Advanced Engineering Informatics, Journal Year: 2020, Volume and Issue: 45, P. 101097 - 101097

Published: April 10, 2020

Language: Английский

Citations

An AIoT-based system for real-time monitoring of tunnel construction DOI

Pin Zhang, Renpeng Chen,

Dai Tian

et al.

Tunnelling and Underground Space Technology, Journal Year: 2020, Volume and Issue: 109, P. 103766 - 103766

Published: Dec. 24, 2020

Language: Английский

Citations

Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns DOI

Pierre Guy Atangana Njock, Shui‐Long Shen, Annan Zhou

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2021, Volume and Issue: 13(6), P. 1500 - 1512

Published: Sept. 14, 2021

A novel and effective artificial neural network (ANN) optimized using differential evolution (DE) is first introduced to provide a robust reliable forecasting of jet grouted column diameters. The proposed computational method adopts the DE algorithm tackle difficulties in training performance networks optimize four quintessential hyper-parameters (i.e. epoch size, number neurons hidden layer, layers, regularization parameter) that govern efficacy. This approach further enhanced by stochastic gradient optimization allow 'expensive' computation efforts. ANN-DE trained prepared grouting dataset, then verified compared with prevalent machine learning tools, i.e. support vector (SVM). results show that, outperforms existing methods for predicting diameter columns since it well balances efficiency model performance. Specifically, achieved root mean square error (RMSE) values 0.90603 0.92813 testing phases, respectively. corresponding were 0.8905 0.9006 ANN, then, 0.87569 0.89968 SVM, paradigm bound be useful solving various geotechnical engineering problems regardless multi-dimension nonlinearity.

Language: Английский

Citations

Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images DOI

Naser Golsanami, Madusanka Nirosh Jayasuriya, Weichao Yan

et al.

Energy, Journal Year: 2021, Volume and Issue: 240, P. 122599 - 122599

Published: Nov. 19, 2021

Language: Английский

Citations