Multifactorial analysis of a gateroad stability at goaf interface during longwall coal mining – A case study DOI Creative Commons
Dmytro Babets,

Olena Sdvyzhkova,

Serhii Hapieiev

et al.

Mining of Mineral Deposits, Journal Year: 2023, Volume and Issue: 17(2), P. 9 - 19

Published: June 30, 2023

Purpose. Creating a generalized algorithm to account for factors (coal seam thickness, enclosed rock mechanical properties, the dimension and bearing capacity of artificial support patterns) causing gateroad state under effect longwall face goaf. Methods. The assessment stability is based on numerical simulation stress-strain (SSS). finite element method used find out changes in SSS surrounding rocks at various stages mining. elastic-plastic constitutive model Hoek-Brown failure criterion implemented codes RS2 RS3 (Rocscience) are applied determine displacements dependently coal strength, width strength (a packwall comprised hardening mixture “BI-lining”). To specify properties material series experimental tests were conducted. A computational experiment dealing with 81 combinations affecting was carried estimate roof slag floor heaving behind face. group data handling (GMDH ) employed generalize relationships between factors. Findings. roof-to-floor closure has been determined intersection goaf packwall, material. It revealed that gains value 30 MPa 3rd day from its beginning use which fully corresponding requirements protective capacity. possibility using untreated mine water liquefy proved, allows simplifying optimizing solute mixing pumping technology. Originality. This study contributes improving understanding influence underground mining operations highlights importance utilizing simulations designs. impact each factor resulting variable (decrease cross-section gate road by height) combinatorial structural identification estimated as follows: 48%, thickness 25%, enclosing 23%, 4%. Practical implications. findings provide stakeholders technique reasonable parameters systems, predictive developed can be mitigate potential instability issues excavations. results have implications similar geological settings valuable design optimization other regions.

Language: Английский

Multi-step prediction model enhanced by adaptive denoising and encoder-decoder for shield machine cutterhead torque in complex conditions DOI

Deming Xu,

Yuan Wang, Jingqi Huang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 158, P. 106398 - 106398

Published: Jan. 18, 2025

Language: Английский

Citations

3

Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning DOI
Shui‐Long Shen, Pierre Guy Atangana Njock, Annan Zhou

et al.

Acta Geotechnica, Journal Year: 2020, Volume and Issue: 16(1), P. 303 - 315

Published: July 2, 2020

Language: Английский

Citations

134

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

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

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

77

Tunnel boring machine vibration-based deep learning for the ground identification of working faces DOI Creative Commons
Mengbo Liu, Shaoming Liao, Yifeng Yang

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2021, Volume and Issue: 13(6), P. 1340 - 1357

Published: Oct. 22, 2021

Tunnel boring machine (TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and itself. In this study, deep recurrent neural networks (RNNs) convolutional (CNNs) were used for vibration-based working face identification. First, field monitoring was conducted to obtain TBM data when tunneling in changing geological conditions, including mixed-face, homogeneous, transmission ground. Next, RNNs CNNs utilized develop prediction models, which then validated using testing dataset. The accuracy of long short-term memory (LSTM) bidirectional LSTM (Bi-LSTM) models approximately 70% with raw data; however, instantaneous frequency transmission, increased 80%. Two types CNNs, GoogLeNet ResNet, trained tested time-frequency scalar diagrams from continuous wavelet transformation. CNN an greater than 96%, performed significantly better RNN models. ResNet-18, 98.28%, best. When sample length set as rotation period, achieved highest while proposed model simultaneously high feedback efficiency. could promptly identify conditions at without stopping normal process, parameters be adjusted optimized timely manner based on predicted results.

Language: Английский

Citations

76

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

Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network DOI
Nan Zhang, Ning Zhang, Qian Zheng

et al.

Acta Geotechnica, Journal Year: 2021, Volume and Issue: 17(4), P. 1167 - 1182

Published: July 30, 2021

Language: Английский

Citations

68

Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison DOI
Pin Zhang, Zhen‐Yu Yin,

Yin-Fu Jin

et al.

Archives of Computational Methods in Engineering, Journal Year: 2021, Volume and Issue: 29(2), P. 1229 - 1245

Published: July 5, 2021

Language: Английский

Citations

64

Machine learning forecasting models of disc cutters life of tunnel boring machine DOI
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hawkar Hashim Ibrahim

et al.

Automation in Construction, Journal Year: 2021, Volume and Issue: 128, P. 103779 - 103779

Published: May 24, 2021

Language: Английский

Citations

61