Numeric mapping of geological stratum topology for shield tunnel
The geological condition above tunnel face is essential to excavation and support. In order to solve the shortcoming of ground borehole method that cannot obtain continuous information while destroying soil stability, this paper presents a numeric algorithm to continuously recover the topology of ea...
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| Published in | IEEE transactions on geoscience and remote sensing Vol. 61; p. 1 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0196-2892 1558-0644 |
| DOI | 10.1109/TGRS.2023.3325444 |
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| Abstract | The geological condition above tunnel face is essential to excavation and support. In order to solve the shortcoming of ground borehole method that cannot obtain continuous information while destroying soil stability, this paper presents a numeric algorithm to continuously recover the topology of each soil stratum. Specifically, firstly, a Silo-Wedge tunnel face model and its topology& mechanics parameter algorithms were proposed based on Terzaghi theory and Mohr stress circle. Through considering the friction effect of cutter-head and designing suitable parabolic arch element contour, the calculated shape and lateral stress ratio closely fits model tests. Secondly, a inter-stratum load transferring algorithm was designed based on differential element force equilibrium model. Through involving arch effect, shearing resistance and friction force, the result become more practical. Thirdly, a bottom-to-top two virtual stratum model was proposed to recursively calculate the thickness change of soil stratum corresponding to sensed pressure deviation. Through designing the load contribution weight of stratum, complex polynomial solving problem is avoided. Finally, a back propagation (BP) neural network was improved based on proposed model characteristics, calculation error and learning time are reduced. Finally, effectiveness was tested. |
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| AbstractList | The geological condition above tunnel face is essential to excavation and support. In order to solve the shortcoming of ground borehole method that cannot obtain continuous information while destroying soil stability, this paper presents a numeric algorithm to continuously recover the topology of each soil stratum. Specifically, firstly, a Silo-Wedge tunnel face model and its topology& mechanics parameter algorithms were proposed based on Terzaghi theory and Mohr stress circle. Through considering the friction effect of cutter-head and designing suitable parabolic arch element contour, the calculated shape and lateral stress ratio closely fits model tests. Secondly, a inter-stratum load transferring algorithm was designed based on differential element force equilibrium model. Through involving arch effect, shearing resistance and friction force, the result become more practical. Thirdly, a bottom-to-top two virtual stratum model was proposed to recursively calculate the thickness change of soil stratum corresponding to sensed pressure deviation. Through designing the load contribution weight of stratum, complex polynomial solving problem is avoided. Finally, a back propagation (BP) neural network was improved based on proposed model characteristics, calculation error and learning time are reduced. Finally, effectiveness was tested. The geological condition above tunnel face is essential to excavation and support. In order to solve the shortcoming of ground borehole method that cannot obtain continuous information while destroying soil stability, this article presents a numeric algorithm to continuously recover the topology of each soil stratum. Specifically, first, a Silo-Wedge tunnel face model and its topology and mechanics parameter algorithms were proposed based on Terzaghi theory and Mohr stress circle. Through considering the friction effect of cutter-head and designing suitable parabolic arch element contour, the calculated shape and lateral stress ratio closely fits model tests. Second, an interstratum load transferring algorithm was designed based on differential element force equilibrium model. Through involving arch effect, shearing resistance, and friction force, the result becomes more practical. Third, a bottom-to-top two virtual stratum model was proposed to recursively calculate the thickness change of soil stratum corresponding to sensed pressure deviation. Through designing the load contribution weight of stratum, complex polynomial solving problem is avoided. Finally, a back propagation (BP) neural network (NN) was improved based on proposed model characteristics, calculation error, and learning time are reduced. Finally, effectiveness was tested. |
| Author | Zhang, Yijie Zhang, Dingli Zhang, Jinyu Shen, Mengru |
| Author_xml | – sequence: 1 givenname: Jinyu orcidid: 0000-0003-3102-7700 surname: Zhang fullname: Zhang, Jinyu organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, CA, China – sequence: 2 givenname: Yijie surname: Zhang fullname: Zhang, Yijie organization: College of Preventive Medic, Army Medical University, Chongqing, CA, China – sequence: 3 givenname: Mengru orcidid: 0000-0002-9683-117X surname: Shen fullname: Shen, Mengru organization: School of Traffic and Transportation, Beijing Jiaotong, Beijing, CA, China – sequence: 4 givenname: Dingli surname: Zhang fullname: Zhang, Dingli organization: School of Civil Engineering, Beijing Jiaotong, Beijing, CA, China |
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| Cites_doi | 10.1016/j.tust.2022.104830 10.1109/TKDE.2018.2861006 10.1109/TGRS.2022.3158660 10.1007/978-3-642-19630-0_46 10.1109/tkde.2020.3001195 10.1109/tkde.2020.3023976 10.1016/j.sandf.2017.03.004 10.1007/s11440-014-0304-5 10.1061/(ASCE)GM.1943-5622.0002404 10.1007/s13369-020-04385-x 10.1016/j.compgeo.2010.11.003 10.1007/s11440-018-0753-3 10.1007/s11440-022-01461-4 10.1007/s12205-021-1254-8 10.1007/s12205-019-0780-0 10.1007/s11709-021-0718-8 10.1201/NOE0415391245.ch34 10.1109/TGRS.2020.2995995 10.1016/j.tust.2022.104405 10.1109/TGRS.2020.3046454 10.1061/(ASCE)GM.1943-5622.0001135 10.1007/s11440-010-0110-7 10.1061/(ASCE)0733-9410(1994)120:7(1148) 10.1016/j.ress.2022.108984 10.1061/(ASCE)GT.1943-5606.0000517 10.1007/s40948-020-00204-7 10.1016/j.tust.2018.06.031 10.1061/40803(187)50 10.1007/s12205-021-0921-0 10.1016/j.compgeo.2018.02.015 10.1016/j.autcon.2023.104813 10.1007/s12665-012-2021-4 10.3390/buildings12040444 10.1007/s10064-020-01878-9 10.1016/j.tust.2023.105104 10.1680/jgeot.18.P.019 10.2112/JCOASTRES-D-20-00094.1 10.1007/s13369-020-05231-w 10.1007/s11440-021-01426-z 10.1016/j.asoc.2023.110206 10.1061/(ASCE)0733-9410(1985)111:3(302) 10.1016/j.compgeo.2014.01.005 10.1007/s11440-022-01590-w 10.1016/j.compgeo.2019.103174 10.1680/geot.2003.53.7.643 10.1016/j.tust.2012.08.001 10.1016/j.tust.2018.01.015 10.1016/j.compgeo.2019.103170 10.1016/j.jrmge.2022.03.002 |
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| SubjectTerms | Algorithms Arches back propagation algorithm Back propagation networks Boreholes Dredging Earth Excavation Faces Friction Friction resistance Geological mapping geological recognition Geology Lateral stress Mathematical models Mechanics Model testing neural network Neural networks Polynomials Shape Shear strength Slope stability Soil Soil stability Soils Stress Stress ratio Terzaghi's theory Topology tunnel face Tunneling shields Tunnels |
| Title | Numeric mapping of geological stratum topology for shield tunnel |
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