Skip to main content
SpringerLink
Log in
Book cover

International Conference on Global Research and Education

INTER-ACADEMIA 2019: Engineering for Sustainable Future pp 361–381Cite as

Urban Train Soil-Structure Interaction Modeling and Analysis

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 101))

Abstract

Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aguirre, E., et al.: Design and implementation of context aware applications with wireless sensor network support in urban train transportation environments. IEEE Sens. J. 17(1), 169–178 (2017)

    Article  Google Scholar 

  2. Bu, B., et al.: Research on method of cooperation among trains for energy saving in urban rail transportation. Tiedao Xuebao/J. China Railw. Soc. 40(8), 43–51 (2018)

    Google Scholar 

  3. Fernández-Rodríguez, A., et al.: Energy efficiency and integration of urban electrical transport systems: EVS and metro-trains of two real European lines. Energies 12(3), 366 (2019)

    Article  Google Scholar 

  4. He, W., et al.: Effect of wind barrier’s height on train-bridge system aerodynamic characteristic of cable-stayed bridge for urban railway transportation. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/J. Central South Univ. (Science and Technology) 48(8), 2238–2244 (2017)

    Google Scholar 

  5. Kim, K.: Exploring the difference between ridership patterns of subway and taxi: case study in Seoul. J. Transp. Geogr. 66, 213–223 (2018)

    Article  Google Scholar 

  6. Tang, T., et al.: VISOS: a visual interactive system for spatial-temporal exploring station importance based on subway data. IEEE Access 6, 42131–42141 (2018)

    Article  Google Scholar 

  7. Wang, J., et al.: IS2Fun: identification of subway station functions using massive urban data. IEEE Access 5, 27103–27113 (2017)

    Article  Google Scholar 

  8. Aram, F., et al.: Design and validation of a computational program for analysing mental maps: aram mental map analyzer. Sustainability (Switzerland) 11(14), 3790 (2019)

    Article  Google Scholar 

  9. Dehghani, M., et al.: Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2), 289 (2019)

    Article  Google Scholar 

  10. Mosavi, A., et al.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7), 1301 (2019)

    Article  Google Scholar 

  11. Nosratabadi, S., et al.: Sustainable business models: a review. Sustainability (Switzerland) 11(6), 1663 (2019)

    Article  Google Scholar 

  12. Taherei Ghazvinei, P., et al.: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng. Appl. Comput. Fluid Mech. 12(1), 738–749 (2018)

    Google Scholar 

  13. Torabi, M., et al.: A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 38(1), 66–76 (2019)

    Article  Google Scholar 

  14. Nosratabadi, S., et al.: Sustainable business models: a review. Sustain. 11(6), 1663 (2019)

    Google Scholar 

  15. Peng, Y.T., Li, Z.C., Choi, K.: Transit-oriented development in an urban rail transportation corridor. Transp. Res. Part B Methodol. 103, 269–290 (2017)

    Article  Google Scholar 

  16. Wu, C., Pei, Y., Gao, J.: Analysis on transportation supply-demand adjustment ability of urban rail transit network. Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/J. Wuhan Univ. Technol. (Transportation Science and Engineering) 41(1), 22–26 and 31 (2017)

    Google Scholar 

  17. Zhang, C., Xia, H., Song, Y.: Rail transportation lead urban form change: a case study of Beijing. Urban Rail Transit. 3(1), 15–22 (2017)

    Article  Google Scholar 

  18. Lee, Y., et al.: Generation characteristics of nanoparticles emitted from subways in operation. Aerosol Air Qual. Res. 18(9), 2230–2239 (2018)

    Article  Google Scholar 

  19. Liu, X., et al.: Evaluation of the utility efficiency of subway stations based on spatial information from public social media. Habitat Int. 79, 10–17 (2018)

    Article  Google Scholar 

  20. Zhao, X., et al.: Clustering analysis of ridership patterns at subway stations: a case in Nanjing, China. J. Urban Planning Dev. 145(2) (2019)

    Article  Google Scholar 

  21. Liu, X., et al.: Evaluation of effects of static pile driving on existing metro tunnel structure. J. Perform. Constr. Facilities 33(4) (2019)

    Article  Google Scholar 

  22. Mostafaei, M., Rezaei Far, A.H., Rastegarnia, A.: Assessment of the impact of case parameters affecting abrasion and brittleness factors in alluviums of line 2 of theTabriz subway, Iran. Bull. Eng. Geology Environ. 78(5), 3851–3861 (2019)

    Article  Google Scholar 

  23. Zhou, S., et al.: An approach integrating dimensional analysis and field data for predicting the load on tunneling machine. KSCE J. Civil Eng. 23(7), 3180–3187 (2019)

    Article  Google Scholar 

  24. Mosleh, A., Nosratabadi, S., Bahrami, P.: Recognizing the business models types in tourism agencies: utilizing the cluster analysis. Int. Bus. Res. 8(2), 173 (2015)

    Article  Google Scholar 

  25. Wang, W., et al.: Assessment of damage in mountain tunnels due to the Taiwan Chi-Chi earthquake. Tunn. Undergr. Space Technol. 16(3), 133–150 (2001)

    Article  Google Scholar 

  26. Kontogianni, V.A., Stiros, S.C.: Earthquakes and seismic faulting: effects on tunnels. Turkish J. Earth Sci. 12(1), 153–156 (2003)

    Google Scholar 

  27. Tajiri, M.: Damage done by the great earthquake disaster of the Hanshin. Awaji district to the Kobe municipal subway system and restoration works of the damage. Japanese Railway Eng. 36(2), 19–23 (1997)

    Google Scholar 

  28. Sharma, S., Judd, W.R.: Underground opening damage from earthquakes. Eng. Geol. 30(3–4), 263–276 (1991)

    Article  Google Scholar 

  29. Hashash, Y.M., et al.: Seismic design and analysis of underground structures. Tunn. Undergr. Space Technol. 16(4), 247–293 (2001)

    Article  Google Scholar 

  30. Hashash, Y.M., Park, D.: Non-linear one-dimensional seismic ground motion propagation in the Mississippi embayment. Eng. Geol. 62(1–3), 185–206 (2001)

    Article  Google Scholar 

  31. Pakbaz, M.C., Yareevand, A.: 2-D analysis of circular tunnel against earthquake loading. Tunn. Undergr. Space Technol. 20(5), 411–417 (2005)

    Article  Google Scholar 

  32. Hashash, Y.M., et al.: Ovaling deformations of circular tunnels under seismic loading, an update on seismic design and analysis of underground structures. Tunn. Undergr. Space Technol. 20(5), 435–441 (2005)

    Article  Google Scholar 

  33. Wang, J.-N., Munfakh, G.: Seismic Design of Tunnels. Vol. 57. WIT Press (2001)

    Google Scholar 

  34. Plaxis, B.: Finite element code for soil and rock analysis. Users Manual, Version, 7 (2000)

    Google Scholar 

  35. Karballaeezadeh, N., et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 13(1), 188–198 (2019)

    Google Scholar 

  36. Mohammadzadeh, S., et al.: Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 4(2), 26 (2019)

    Article  Google Scholar 

  37. Haase, D., et al.: Global urbanization. The Urban Planet: Knowledge Towards Sustainable Cities, 19 (2018)

    Google Scholar 

  38. Brinkgreve, R. et al.: PLAXIS 2D 2010. User manual, Plaxis bv (2010)

    Google Scholar 

  39. Manual, P.: Finite Element Code for Soil and Rock Analysis. Published and distributed by AA Balkema Publishers, Nederland’s Comput. Geotech. 32(5): p. 326–339 (2007)

    Google Scholar 

  40. Asadi, E., et al.: Groundwater Quality Assessment for Drinking and Agricultural Purposes in Tabriz Aquifer, Iran (2019)

    Google Scholar 

  41. Asghar, M.Z., Subhan, F., Imran, M., Kundi, F.M., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content. Pre-prints 2019, 2019080019 https://doi.org/10.20944/preprints201908.0019.v1

  42. Bemani, A., Baghban, A., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, Applying, A.R.: ANN, ANFIS, and LSSVM Models for Estimation of Acid Sol-vent Solubility in Supercritical CO2. Preprints 2019, 2019060055. https://doi.org/10.20944/preprints201906.0055.v2

  43. Choubin, B., et al.: Snow avalanche hazard prediction using machine learning methods. J. Hydrol., 577 (2019)

    Article  Google Scholar 

  44. Choubin, B., et al.: An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019)

    Article  Google Scholar 

  45. Dineva, A., et al.: Multi-Label classification for fault diagnosis of rotating electrical machines (2019)

    Google Scholar 

  46. Ghalandari, M., et al.: Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis. Eng. Appl. Comput. Fluid Mech. 13(1), 519–528 (2019)

    Google Scholar 

  47. Ghalandari, M., et al.: Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech. 13(1), 804–810 (2019)

    Google Scholar 

  48. Mosavi, A., Edalatifar, M.: A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration, in Lecture Notes in Networks and Systems, pp. 235–243, Springer (2019)

    Google Scholar 

  49. Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: literature review. Water (Switzerland) 10(11) (2018)

    Article  Google Scholar 

  50. Mosavi, A., Rabczuk, T.: Learning and intelligent optimization for material design innovation, D.E. Kvasov, et al., Editors, pp. 358–363. Springer (2017)

    Google Scholar 

  51. Mosavi, A., Rabczuk, T., Várkonyi-Kóczy, A.R.: Reviewing the novel machine learning tools for materials design, D. Luca, L. Sirghi, and C. Costin, Editors, pp. 50–58. Springer (2018)

    Google Scholar 

  52. Mosavi, A., et al.: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Eng. Appl. Comput. Fluid Mech. 13(1), 482–492 (2019)

    Google Scholar 

  53. Mosavi, A., Várkonyi-Kóczy, A.R.: Integration of machine learning and optimization for robot learning, R. Jablonski and R. Szewczyk, Editors, pp. 349–355. Springer (2017)

    Google Scholar 

  54. Qasem, S.N., et al.: Estimating daily dew point temperature using machine learning algorithms. Water (Switzerland) 11(3) (2019)

    Article  Google Scholar 

  55. Rezakazemi, M., Mosavi, A., Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476 (2019)

    Article  Google Scholar 

  56. Riahi-Madvar, H., et al.: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Eng. Appl. Comput. Fluid Mech. 13(1), 529–550 (2019)

    Google Scholar 

  57. Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T., Várkonyi-Kóczy, A.R.: Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints 2019, 2019070351. https://doi.org/10.20944/preprints201907.0351.v1

  58. Shamshirband, S., Hadipoor, M., Baghban, A., Mosavi, A., Bukor J., Várkonyi-Kóczy, A.R.: Developing an ANFIS-PSO Model to predict mercury emissions in Combustion Flue Gases. Preprints 2019, 2019070165. https://doi.org/10.20944/preprints201907.0165.v1

  59. Shamshirband, S., et al.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. Appl. Comput. Fluid Mech. 13(1), 91–101 (2019)

    Google Scholar 

  60. Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier. arXiv preprint arXiv:1906.08863 (2019)

  61. Torabi, M., et al.: A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation, in Lecture Notes in Networks and Systems, pp. 266–274. Springer (2019)

    Google Scholar 

  62. Ardabili, S., Mosavi, A., Mahmoudi, Mesri Gundoshmian, T., Nosratabadi, S., Var-konyi-Koczy, A.: Modelling temperature variation of mushroom growing hall using artificial neural networks, Preprints 2019

    Google Scholar 

  63. Mesri Gundoshmian, T., Ardabili, S., Mosavi, A., Varkonyi-Koczy, A., Prediction of combine harvester performance using hybrid machine learning modeling and re-sponse surface methodology, Preprints 2019

    Google Scholar 

  64. Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Systematic review of deep learning and machine learning models in biofuels research, Preprints 2019

    Google Scholar 

  65. Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Advances in machine learning modeling reviewing hybrid and ensemble methods, Preprints 2019

    Google Scholar 

  66. Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Building Energy information: demand and consumption prediction with Machine Learning models for sustainable and smart cities, Preprints 2019

    Google Scholar 

  67. Ardabili, S., Mosavi, A., Dehghani, M., Varkonyi-Koczy, A.: Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review, Preprints 2019

    Google Scholar 

  68. Mohammadzadeh, D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Várkonyi-Kóczy A.: Urban Train Soil-Structure Interaction Modeling and Analysis, Preprints 2019

    Google Scholar 

  69. Mosavi, A., Ardabili, S., Varkonyi-Koczy, A.: List of deep learning models, Preprints 2019

    Google Scholar 

  70. Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability, Preprints 2019

    Google Scholar 

Download references

Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Danial Mohammadzadeh

  2. Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad, Iran

    Danial Mohammadzadeh

  3. Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

    Nader Karballaeezadeh

  4. Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Morteza Mohemmi

  5. Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, 1034, Hungary

    Amir Mosavi & Annamária R. Várkonyi-Kóczy

  6. School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK

    Amir Mosavi

  7. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamária R. Várkonyi-Kóczy

Authors
  1. Danial Mohammadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nader Karballaeezadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Morteza Mohemmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amir Mosavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annamária R. Várkonyi-Kóczy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Mosavi .

Editor information

Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohammadzadeh, D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Várkonyi-Kóczy, A.R. (2020). Urban Train Soil-Structure Interaction Modeling and Analysis. In: Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, vol 101. Springer, Cham. https://doi.org/10.1007/978-3-030-36841-8_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36841-8_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36840-1

  • Online ISBN: 978-3-030-36841-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation