Review Article
Progress on Friction-contact Mechanics Research: From Single-to Multi-Asperity
Kang Yu*
Issue:
Volume 13, Issue 1, March 2026
Pages:
1-9
Received:
5 January 2026
Accepted:
15 January 2026
Published:
29 January 2026
Abstract: Friction is a ubiquitous physical phenomenon in nature and daily life. The demand for energy conservation and green development in industrial development has led to increasing attention being paid to tribology research. Despite significant achievements in tribology, many friction problems remain unsolved, particularly the lack of a unified friction theory applicable to all friction issues. The fundamental conditions for occurrence of friction are contact and relative motion between two surfaces, so the advancement of contact theories has significantly contributed to the development of tribology. Not only the models of contact theory across different scales in the past development of tribology were reviewed in this paper, also the assumptions and applicability limitations of each model were summarized and compared. The limitations of theoretical calculations of friction-contact research have been concerned. Now the study of contact mechanics and tribology is facing new opportunities for further progress under the era of the emergence of the big data and artificial intelligence based on sufficient data and computing power. The preliminary prospect on the research of tribology has been carried out. A novel research strategy that combines artificial intelligence, experimentation, and contact theory was proposed to investigate the contact problem in tribology field. This strategy may also be online collaborative research between different research groups all over the world.
Abstract: Friction is a ubiquitous physical phenomenon in nature and daily life. The demand for energy conservation and green development in industrial development has led to increasing attention being paid to tribology research. Despite significant achievements in tribology, many friction problems remain unsolved, particularly the lack of a unified friction...
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Research Article
Rolling Window Deep Learning for Hard-rock TBM Penetration Rate Prediction
Nantapol Monthanopparat*,
Tawatchai Tanchaisawat
Issue:
Volume 13, Issue 1, March 2026
Pages:
10-21
Received:
4 May 2026
Accepted:
14 May 2026
Published:
26 May 2026
DOI:
10.11648/j.ajma.20261301.12
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Abstract: Penetration rate prediction for hard rock tunnel boring machines (TBMs) remains challenging because rock mass conditions and machine operating regimes vary continuously along the tunnel alignment. Conventional static prediction models may not adequately represent such non-stationary construction conditions, particularly when geotechnical measurements are incomplete or obtained with delay during excavation. This study aims to develop a construction-phase prediction framework for ring-scale TBM penetration rate in a granite tunnel drive by integrating geotechnical data completion, sequence deep learning, and rolling-window model evaluation. A dataset of 1,000 consecutive rings was compiled, including boring-only penetration rate, thrust, torque, cutterhead rotational speed, rock mass type, and uniaxial compressive strength (UCS). Missing UCS measurements were completed using inverse distance weighting within a block model representation, resulting in estimated UCS values of approximately 33–177 MPa for rock masses dominated by massive and fractured granite. Three sequence deep learning models, namely long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional network (TCN), were evaluated using root mean square error (RMSE), mean absolute error (MAE), and a symmetric ±10% tolerance band adapted from accuracy-band concepts used in AACE-based project controls. The proposed rolling protocol used 100-ring validation and 100-ring test blocks to assess predictive performance under changing ground conditions. The results show that the optimized GRU model provided the most robust overall performance, achieving a mean test RMSE of approximately 0.229 m/h and a mean within-band compliance of approximately 54% across rolling folds. These findings indicate that rolling-window sequence learning can provide a practical and adaptable framework for construction-phase TBM performance prediction under evolving geological and operational conditions.
Abstract: Penetration rate prediction for hard rock tunnel boring machines (TBMs) remains challenging because rock mass conditions and machine operating regimes vary continuously along the tunnel alignment. Conventional static prediction models may not adequately represent such non-stationary construction conditions, particularly when geotechnical measuremen...
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