Reliability assessment of EPB tunnel-related settlement
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2010/03/25
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Journal
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Indian UGC (journal)
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Refrences17
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Citations17
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Anthony T.C. Goh
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A.M. Hefney
Cite
Goh, Anthony T.C., and A.M. Hefney. “Reliability Assessment of EPB Tunnel-Related Settlement”. Geomechanics and Engineering, vol. 2, no. 1, 2010, pp. 57-69, https://doi.org/10.12989/gae.2010.2.1.057.
Goh, A. T., & Hefney, A. (2010). Reliability assessment of EPB tunnel-related settlement. Geomechanics and Engineering, 2(1), 57-69. https://doi.org/10.12989/gae.2010.2.1.057
Goh AT, Hefney A. Reliability assessment of EPB tunnel-related settlement. Geomechanics and Engineering. 2010;2(1):57-69.
Journal Categories
Technology
Engineering (General)
Civil engineering (General)
Technology
Engineering (General)
Civil engineering (General)
Engineering geology
Rock mechanics
Soil mechanics
Underground construction
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Artificial neural networks analysis of São Paulo subway tunnel settlement data | Tunnelling and Underground Space Technology |
| 105 | 2008 |
| 10.1061/(ASCE)1090-0241(2006)132:3(337) | Journal of Geotechnical and Geoenvironmental Engineering |
| 2006 | |
| Reliability assessment of serviceability performance of braced retaining walls using a neural network approach | International Journal for Numerical and Analytical Methods in Geomechanics |
| 46 | 2005 |
| 10.1016/S0167-4730(03)00023-7 | 2004 | |||
Local settlements and sinkholes due to EPB tunnelling | Proceedings of the Institution of Civil Engineers - Geotechnical Engineering |
| 32 | 2003 |
Refrences Analysis
The category
Technology: Engineering (General). Civil engineering (General) 10
is the most frequently represented among the references in this article. It primarily includes studies from Canadian Geotechnical Journal and Journal of Geotechnical and Geoenvironmental Engineering. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods | Underground Space | 2024 | ||
| Mathematical model for approximating shield tunneling-induced surface settlement via multi-gene genetic programming | Acta Geotechnica |
| 4 | 2023 |
Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction | Applied Sciences |
| 8 | 2023 |
| Application of artificial intelligence in geotechnical engineering: A state-of-the-art review | Earth-Science Reviews |
| 72 | 2022 |
| Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets | Journal of Rock Mechanics and Geotechnical Engineering |
| 21 | 2022 |
Citations Analysis
The category
Science: Geology 8
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Reliability analysis of serviceability performance for an underground cavern using a non-intrusive stochastic method and was published in 2013. The most recent citation comes from a 2024 study titled Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods. This article reached its peak citation in 2022, with 4 citations. It has been cited in 16 different journals, 18% of which are open access. Among related journals, the Journal of Rock Mechanics and Geotechnical Engineering cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.