PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | 28 | 2 |
Tytuł artykułu

Multi-index classification model for loess deposits based on rough set and BP neural network

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Classifying loess deposits is an important process for selecting support form and construction methods for tunnels. An accurate evaluation of loess deposits is a necessary prerequisite to control deformation, save cost, and improve construction efficiency. In this paper, a neural network model with an evaluation system consisting of physical and mechanical indices of loess is proposed to realize intelligent classification of loess deposits for tunneling. The influence of water content, natural density, cohesion, internal friction angle, elastic modulus, and Poisson ratio on stability level of loess is analyzed by rough set theory based on statistical data of borehole samples. Results show that the affect of natural density is negligible. Then other indicators such as input nodes and the BP neural network model are formed after learning statistical samples and being applied to the project for testing. Finally, the output of the model is consistent with the actual. This study provides a multi-index model for evaluating loess deposits surrounding tunnels and provides a reference for future research.
Słowa kluczowe
Wydawca
-
Rocznik
Tom
28
Numer
2
Opis fizyczny
p.953-963,fig.,ref.
Twórcy
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
autor
  • Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, China
Bibliografia
  • 1. YATES K., FENTON C.H., BELL D.H. A review of the geotechnical characteristics of loess and loess-derived soils from canterbury, south island, new zealand. Engineering Geology, 2017.
  • 2. HAO Q.Z. Onset of asian desertification by 22 myr ago inferred from loess deposits in china. Nature, 416 (6877), 159, 2002.
  • 3. TUO D., XU M., LI Q., LIU S. Soil aggregate stability and associated structure affected by long-term fertilization for a loessial soil on the loess plateau of china. Polish Journal of Environmental Studies, 26 (2), 827, 2017.
  • 4. LI Q., LIU G., ZHANG Z., TUO D., MIAO X. Structural stability and erodibility of soil in an age sequence of artificial robinia pseudoacacia on a hilly loess plateau. Polish Journal of Environmental Studies, 25 (4), 1595, 2016.
  • 5. LIU T., LIU G.B., NG C.W.W., HONG Y. Ground deformations and soil-structure interaction of a multipropped excavation in shanghai soft clays. Géotechnique, (62), 907, 2012.
  • 6. LI Y. A review of shear and tensile strengths of the malan loess in china. Engineering Geology, 2017.
  • 7. ROGERS C.D.F., DIJKSTRA T.A., SMALLEY I.J. Particle packing from an earth science viewpoint. Earth-Science Reviews, 36 (1-2), 59, 1994.
  • 8. KRUSE G.A.M., DIJKSTRA T.A., SCHOKKING F. Effects of soil structure on soil behaviour: illustrated with loess, glacially loaded clay and simulated flaser bedding examples. Engineering Geology, 91 (1), 34, 2007.
  • 9. XUE Y., ZHANG X., LI S., QIU D., SU M., LI L., LI Z., TAO Y. Analysis of factors influencing tunnel deformation in loess deposits by data mining: a deformation prediction model. Engineering Geology, 232, 94, 2018.
  • 10. XUE X.H., SU Z.M., SUN Z.J., SONG F. Analysis of the tunnel disease considering the unsaturated loess matric suction effects. Advanced Materials Research, 859, 182-, 2014.
  • 11. JEFFERSON I.F., EVSTATIEV D., KARASTANEV D., MAVLYANOVA N.G., SMALLEY I.J. Engineering geology of loess and loess-like deposits: a commentary on the russian literature. Engineering Geology, 68 (3), 333, 2003.
  • 12. REZNIK Y.M. Influence of physical properties on deformation characteristics of collapsible soils. Engineering Geology, 92 (1), 27, 2007.
  • 13. FENG S.J., DU F.L., SHI Z.M., SHUI W.H., TAN K. Field study on the reinforcement of collapsible loess using dynamic compaction. Engineering Geology, 185, 105, 2015.
  • 14. WEN X., CHEN M., FENG W., HUANG C. Mid-late holocene climatic changes recorded by loess deposits in the eastern margin of the tibetan plateau: implication for human migrations. Quaternary International, 2017.
  • 15. LI P., ZHAO Y., ZHOU X. Displacement characteristics of high-speed railway tunnel construction in loess ground by using multi-step excavation method. Tunnelling and Underground Space Technology, 51, 41, 2016.
  • 16. JIA C., LI Y., LIAN M., ZHOU X. Jointed surrounding rock mass stability analysis on an underground cavern in a hydropower station based on the extended key block theory. Energies, 10 (4), 563, 2017.
  • 17. XU N.W., LI T.B., DAI F., LI B., ZHU Y.G., YANG D.S. Microseismic monitoring and stability evaluation for the large scale underground caverns at the houziyan hydropower station in southwest china. Engineering Geology, 188, 48, 2015.
  • 18. SHIOTANI T. Evaluation of long-term stability for rock slope by means of acoustic emission technique. Ndt & E International, 39 (3), 217, 2006.
  • 19. BAYRAM A., KANKAL M. Artificial neural network modeling of dissolved oxygen concentrations in a turkish watershed. Polish Journal of Environmental Studies, 24 (4), 1507, 2015.
  • 20. HALECKI W., MLYNSKI D., RYCZEK M., KRUK E., RADECKI-PAWLIK A. Applying an artificial neural network (ann) to assess soil salinity and temperature variability in agricultural areas of a mountain catchment. Polish Journal of Environmental Studies, 2017.
  • 21. FINES J.M., AGAH A. Machine tool positioning error compensation using artificial neural networks. Engineering Applications of Artificial Intelligence, 21 (7), 1013, 2008.
  • 22. QIU D.H., CHEN J.P., QUE J.S. Evaluation of tunnel rock quality with rough sets theory and artificial neural networks. Journal of Jilin University, 38 (1), 86, 2008 [In Chinese].
  • 23. WANG X.T., LI S.C., MA X.Y., XUE Y.G., HU J., LI Z.Q. Risk Assessment of Rockfall Hazards in a Tunnel Portal Section Based on Normal Cloud Model. Polish Journal of Environmental Studies, 26 (5), 2017.
  • 24. RUSEK J. Support vector machines and probabilistic neural networks in the assessment of the risk of damage to water supply systems in mining areas. Polish Journal of Environmental Studies, 25 (5A), 71, 2016.
  • 25. GIOVANIS D.G., PAPADOPOULOS V. Spectral representation-based neural network assisted stochastic structural mechanics. Engineering Structures, 84, 382, 2015.
  • 26. PIOTROWSKI A.P., NAPIORKOWSKI M.J., NAPIORKOWSKI J.J., OSUCH M. Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology, 529 (1), 302, 2015.
  • 27. COMERFORD L., KOUGIOUMTZOGLOU I.A., BEER M. An artificial neural network approach for stochastic process power spectrum estimation subject to missing data. Structural Safety, 52, 150, 2015.
  • 28. DEMIR S., KARADENIZ A., DEMIR N.M. Using steepness coefficient to improve artificial neural network performance for environmental modeling. Polish Journal of Environmental Studies, 25 (4), 2016.
  • 29. PAWLAK Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1992.
  • 30. DAI C.Y. A survey on rough set theory and its application. Journal of Yuzhou University, 2004 [In Chinese].
  • 31. LAZO-CORTES M.S., MARTINEZ-TRINIDAD J.F., CARRASCO-OCHOA J.A., SANCHEZ-DIAZ G. On the relation between rough set reducts and typical testors. Information Sciences, 294, 152, 2015.
  • 32. DOU H., YANG X., SONG X., YU H., WU W.Z., YANG J. Decision-theoretic rough set: a multicost strategy. Knowledge-Based Systems, 91, 71, 2016.
  • 33. ZHANG W.X. Rough Set Theory and Method. Science Press, 2001 [In Chinese].
  • 34. XU W., LI A.W. Multigranulation decision-theoretic rough set in ordered information system. Fundamenta Informaticae, 139 (1), 67, 2015.
  • 35. XU W., GUO Y. Generalized multigranulation double-quantitative decision-theoretic rough set. Knowledge-Based Systems, 105, 190, 2016.
  • 36. ALI R., SIDDIQI M.H., LEE S. Rough set-based approaches for discretization: a compact review. Artificial Intelligence Review, 44 (2), 235, 2015.
  • 37. DUNTSCH I., GEDIGA G. Statistical evaluation of rough set dependency analysis. International Journal of Human-Computer Studies, 46 (5), 589, 1997.
  • 38. LIANG J.Y., KAI-SHE Q.U., ZONG-BEN X.U. Reduction of attribute in information systems. Systems Engineering-theory & Practice, 2001 [In Chinese].
  • 39. LUAN X.Y., LI Z.P., LIU T.Z. A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm. Neurocomputing, 174, 522, 2016.
  • 40. VIEIRA J., DIAS F.M., MOTA A. Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study. Engineering Applications of Artificial Intelligence, 17 (3), 265, 2004.
  • 41. YI Y., LU W., HONG D., LIU H., ZHANG L. Application of dual-response surface methodology and radial basis function artificial neural network on surrogate model of the groundwater flow numerical simulation. Polish Journal of Environmental Studies, 2017.
  • 42. CH S., MATHUR S. Particle swarm optimization trained neural network for aquifer parameter estimation. KSCE Journal of Civil Engineering, 16 (3), 298, 2012.
  • 43. YOU K. A case study on the utilization of tunnel face mapping data for a back analysis based on artificial neural network. KSCE Journal of Civil Engineering, 18 (3), 751, 2014.
  • 44. REN X.C., LAI Y.M., ZHANG F.Y., HU K. Test method for determination of optimum moisture content of soil and maximum dry density. KSCE Journal of Civil Engineering, 19 (7), 2061, 2015.
  • 45. ZHAO Y., LI G.L., YU Y. Loess tunnel engineering. China Railway Press, 50, 2011 [In Chinese].
  • 46. LI Y., SONG Y., CHEN X., LI J., MAMADJANOV Y., AMINOV J. Geochemical composition of tajikistan loess and its provenance implications. Palaeogeography Palaeoclimatology Palaeoecology, 446, 186, 2016.
  • 47. WANG B., KAAKINEN A., CLIFT P.D. Tectonic controls of the onset of aeolian deposits in chinese loess plateau – a preliminary hypothesis. International Geology Review, (5), 1, 2017.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-1d6f98e5-6946-4218-b22c-d2000af478a6
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.