Skip to main content
SpringerLink
Log in

Prediction of underground metro train-induced ground vibration using hybrid PSO-ANN approach

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The soil conditions substantially influence the intensity of underground metro train-induced ground vibration. However, assessing the influence of metro train-induced vibration on nearby structures is difficult due to problems in acquiring soil characteristics’ field data. An optimized artificial neural network (ANN)-based hybrid prediction model is proposed to predict the underground metro train-induced ground vibration to address this issue. Particle swarm optimization (PSO) and genetic algorithm (GA) are used in the hybrid model to optimize the neural network’s weights and biases. The datasets obtained by the validated numerical model are used to train the neural network. An explicit time domain, two-dimensional (2D) numerical model is developed using the finite element method (FEM) based on a two-step methodology and validated with experimental results of Delhi metro sites. Good agreement is observed between the numerical and experimental results in both time and frequency domains. The proposed hybrid PSO-ANN and GA-ANN model considered several soil properties such as density, Poisson’s ratio, damping, Young’s modulus and wave speed while predicting train-induced ground vibration. The PSO-ANN model predicted the outcome with an average error of 0.86%. Moreover, the proposed PSO-ANN model is also compared with the Federal Transit Administration’s (FTA) semi-empirical approach of vibration assessment, linear support vector machine (SVM) and classification and regression tree (CART). The hybrid PSO-ANN model predicted the outcomes with greater accuracy than the basic ANN model, linear SVM, CART, GA-ANN and FTA’s approach. Additionally, the hybrid model can consider the effect of parameters such as vehicle characteristics, suspension system, speed and tunnel depth.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

\({M}_{\mathrm{v}}\) :

Mass matrix of vehicle

\({C}_{\mathrm{v}}\) :

Damping matrix of vehicle

\({K}_{\mathrm{v}}\) :

Stiffness matrix of vehicle

\({P}_{\mathrm{v}}\) :

Vehicle force vector

\({\ddot{q}}_{\mathrm{v}}\) :

Vehicle acceleration vector

\({\dot{q}}_{\mathrm{v}}\) :

Vehicle velocity vector

\({q}_{\mathrm{v}}\) :

Vehicle displacement vector

\({M}_{\mathrm{t}}\) :

Mass matrix of the track-tunnel structure

\(G\) :

Shear modulus

\(\rho\) :

Mass density of soil

\({\omega }_{\mathrm{l}},{\omega }_{\mathrm{u}}\) :

Lower and upper limit of frequency of interest

\({Vs}_{\mathrm{min}}\) :

Minimum shear wave velocity

\(\eta\) :

Constant parameter to define the nature of the artificial boundary

\({C}_{\mathrm{b}}\) :

Damping constant of artificial boundary

\({V}_{\mathrm{dB}}\) :

Vibration level in decibel

\({P}_{i}^{h}\) :

iTh Particle position at the hth iteration

\({y}_{1}\) :

Self-adjustment weighing factor

\(y\) :

Numerical values

\({y}_{m}\) :

Mean values

\({p}_{\mathrm{best}}\) :

Personal best

\({C}_{\mathrm{t}}\) :

Damping matrix of the track-tunnel structure

\({K}_{\mathrm{t}}\) :

Stiffness matrix of the track-tunnel structure

\({P}_{\mathrm{t}}\) :

Track-tunnel structure force vector

\({\ddot{q}}_{\mathrm{t}}\) :

Track-tunnel structure acceleration vector

\({\dot{q}}_{\mathrm{t}}\) :

Track-tunnel structure velocity vector

\({q}_{\mathrm{t}}\) :

Track-tunnel structure displacement vector

\(N\) :

Total degrees of freedom of track subsystem

\(\alpha ,\beta\) :

Rayleigh damping parameters

\(R\) :

Distance between vibration source and artificial boundary

\(c\) :

P-wave velocity in a normal direction or S-wave velocity in a tangential direction

\({l}_{\mathrm{max}}\) :

Maximum size of finite element model

\({f}_{\mathrm{max}}\) :

Maximum frequency of interest

\({K}_{\mathrm{b}}\) :

Spring constant of artificial boundary

\(D\) :

Damping ratio

\({V}_{\mathrm{rms}}\) :

Root-mean-square velocity

\({V}_{i}\) :

Velocity of an ith particle

\({y}_{2}\) :

Social-adjustment weighing factor

y′:

Predicted values

N′:

Total number of samples

\({g}_{\mathrm{best}}\) :

Global best

References

  1. Quagliata A, Ahearn M, Boeker E, Roof C, Meister L, Singleton H (2018) Transit noise and vibration impact assessment manual. Cambridge

  2. Ministry of Railways I (2015) Metro rail transit system guidelines for noise and vibrations. Lucknow

  3. ISO (2003) ISO 2631-2:2003 Mechanical vibration and shock—evaluation of human exposure to whole-body vibration—part 2: vibration in buildings (1 Hz to 80 Hz)

  4. Xu Q, Xiao Z, Liu T, Lou P, Song X (2015) Comparison of 2D and 3D prediction models for environmental vibration induced by underground railway with two types of tracks. Comput Geotech 68:169–183. https://doi.org/10.1016/j.compgeo.2015.04.011

    Article  Google Scholar 

  5. Fryba L (1995) History of winkler foundation. Veh Syst Dyn 24:7–12. https://doi.org/10.1080/00423119508969611

    Article  Google Scholar 

  6. Ruge P, Birk C (2007) A comparison of infinite Timoshenko and Euler–Bernoulli beam models on Winkler foundation in the frequency- and time-domain. J Sound Vib 304:932–947. https://doi.org/10.1016/j.jsv.2007.04.001

    Article  Google Scholar 

  7. Sun H, Fang Y, Yuan Z, Weng Z, Ni D (2021) Analytical modeling for the calculation of underground train-induced vibrations in inhomogeneous soils with uncertainty. AIP Adv 11:1–14. https://doi.org/10.1063/5.0058234

    Article  Google Scholar 

  8. Hussein M, Hunt H, Kuo K, Costa PA, Barbosa J (2015) The use of sub-modelling technique to calculate vibration in buildings from underground railways. Proc Inst Mech Eng Part F J Rail Rapid Transit 229:303–314. https://doi.org/10.1177/0954409713511449

    Article  Google Scholar 

  9. Zhou S, He C, Di H, Guo P, Zhang X (2017) An efficient method for predicting train-induced vibrations from a tunnel in a poroelastic half-space. Eng Anal Bound Elem 85:43–56. https://doi.org/10.1016/j.enganabound.2017.09.013

    Article  MathSciNet  MATH  Google Scholar 

  10. Xu Q, Ou X, Au FTK, Lou P, Xiao Z (2016) Effects of track irregularities on environmental vibration caused by underground railway. Eur J Mech A/Solids 59:280–293. https://doi.org/10.1016/j.euromechsol.2016.04.005

    Article  Google Scholar 

  11. Real T, Zamorano C, Ribes F, Real JI (2015) Train-induced vibration prediction in tunnels using 2D and 3D FEM models in time domain. Tunn Undergr Space Technol 49:376–383. https://doi.org/10.1016/j.tust.2015.05.004

    Article  Google Scholar 

  12. Bian X, Chen Y, Hu T (2008) Numerical simulation of high-speed train induced ground vibrations using 2.5D finite element approach. Sci China Ser G Phys Mech Astron 51:632–650. https://doi.org/10.1007/s11433-008-0060-3

    Article  Google Scholar 

  13. Xing M, Zhao C, Wang P, Lu J, Yi Q (2019) A numerical analysis of ground vibration induced by typical rail corrugation of underground subway. Shock Vib 2019:1–16. https://doi.org/10.1155/2019/8406813

    Article  Google Scholar 

  14. Kuhlemeyer RL, Lysmer J (1973) Finite element method accuracy for wave propagation problems. J Soil Mech Found Div 99:421–427

    Article  Google Scholar 

  15. Deeks AJ, Randolph MF (1994) Axisymmetric time-domain transmitting boundaries. J Eng Mech 120:25–42. https://doi.org/10.1061/(ASCE)0733-9399(1994)120:1(25)

    Article  Google Scholar 

  16. Bettess P (1977) Infinite elements. Int J Numer Methods Eng 11:53–64. https://doi.org/10.1002/nme.1620110107

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhang J, Yan Q, Sun M, Li B, Chen W, Chen H (2021) Experimental study on the vibration damping of two parallel shield tunnels connected by an assembled transverse passage. Tunn Undergr Space Technol 107:103659. https://doi.org/10.1016/j.tust.2020.103659

    Article  Google Scholar 

  18. Yang J, Zhu S, Zhai W, Kouroussis G, Wang Y, Wang K et al (2019) Prediction and mitigation of train-induced vibrations of large-scale building constructed on subway tunnel. Sci Total Environ 668:485–499. https://doi.org/10.1016/j.scitotenv.2019.02.397

    Article  Google Scholar 

  19. Gjelstrup H, Alcover IF, Andersen JE, Larsen A (2016) Probabilistic empirical model for train-induced vibrations. Proc Inst Civ Eng Struct Build 169:563–573. https://doi.org/10.1680/jstbu.15.00010

    Article  Google Scholar 

  20. Arcos R, Soares PJ, Alves Costa P, Godinho L (2021) An experimental/numerical hybrid methodology for the prediction of railway-induced ground-borne vibration on buildings to be constructed close to existing railway infrastructures: numerical validation and parametric study. Soil Dyn Earthq Eng 150:1–16. https://doi.org/10.1016/j.soildyn.2021.106888

    Article  Google Scholar 

  21. Kuppelwieser H, Ziegler A (1996) A tool for predicting vibration and structure-borne noise immissions caused by railways. J Sound Vib 193:261–267. https://doi.org/10.1006/jsvi.1996.0266

    Article  Google Scholar 

  22. Sadeghi J, Esmaeili MH, Akbari M (2019) Reliability of FTA general vibration assessment model in prediction of subway induced ground borne vibrations. Soil Dyn Earthq Eng 117:300–311. https://doi.org/10.1016/j.soildyn.2018.11.002

    Article  Google Scholar 

  23. Di MG, Giunta M, Di LCM (2009) Assessing the open trenches in screening railway ground-borne vibrations by means of artificial neural network. Adv Acoust Vib 2009:1–12. https://doi.org/10.1155/2009/942787

    Article  Google Scholar 

  24. Connolly DP, Kouroussis G, Giannopoulos A, Verlinden O, Woodward PK, Forde MC (2014) Assessment of railway vibrations using an efficient scoping model. Soil Dyn Earthq Eng 58:37–47. https://doi.org/10.1016/j.soildyn.2013.12.003

    Article  Google Scholar 

  25. Yao J, Yao B, Du Y, Jiang Y (2014) Train-induced vibration prediction in multi-story buildings using support vector machine. Neural Netw World 24:89–102. https://doi.org/10.14311/NNW.2014.24.005

    Article  Google Scholar 

  26. Zhao Y, Guo ZH, Yan JM (2017) Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks. J Vibroengineering 19:2456–2474. https://doi.org/10.21595/jve.2017.17238

    Article  Google Scholar 

  27. Paneiro G, Durão FO, Costa e Silva M, Falcão Neves P (2018) Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas. Neural Comput Appl 29:1045–1057. https://doi.org/10.1007/s00521-016-2625-9

    Article  Google Scholar 

  28. Jayawardana P, Thambiratnam DP, Perera N, Chan T, Subashi De Silva GHMJ (2019) Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches. Soils Found 59:874–887. https://doi.org/10.1016/j.sandf.2019.03.004

    Article  Google Scholar 

  29. Liang R, Liu W, Ma M, Liu W (2021) An efficient model for predicting the train-induced ground-borne vibration and uncertainty quantification based on Bayesian neural network. J Sound Vib 495:115908. https://doi.org/10.1016/j.jsv.2020.115908

    Article  Google Scholar 

  30. Fang L, Yao J, Xia H (2019) Prediction on soil-ground vibration induced by high-speed moving train based on artificial neural network model. Adv Mech Eng 11:1–10. https://doi.org/10.1177/1687814019847290

    Article  Google Scholar 

  31. Majumder M, Bhattacharyya S (2021) ANN-Based model to predict the screening efficiency of EPS geofoam filled trench in reducing high-speed train-induced vibration. In: Sitharam TG, Kolathayar S, Sharma ML (eds) Seism. Hazards risk. Springer Singapore, Singapore, pp 167–175. https://doi.org/10.1007/978-981-15-9976-7_16

    Chapter  Google Scholar 

  32. Azimi Y, Khoshrou SH, Osanloo M (2019) Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement 147:1–17. https://doi.org/10.1016/j.measurement.2019.106874

    Article  Google Scholar 

  33. Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. In: Proc 5th Indian Int Conf Artif Intell IICAI 2011, pp 231–44

  34. Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75:27–36. https://doi.org/10.1007/s10064-015-0720-2

    Article  Google Scholar 

  35. Yarmohammadi F, Rafiee-Dehkharghani R, Behnia C, Aref AJ (2019) Design of wave barriers for mitigation of train–induced vibrations using a coupled genetic-algorithm/finite-element methodology. Soil Dyn Earthq Eng 121:262–275. https://doi.org/10.1016/j.soildyn.2019.03.007

    Article  Google Scholar 

  36. Ticona Melo LR, Ribeiro D, Calçada R, Bittencourt TN (2020) Validation of a vertical train–track–bridge dynamic interaction model based on limited experimental data. Struct Infrastruct Eng 16:181–201. https://doi.org/10.1080/15732479.2019.1605394

    Article  Google Scholar 

  37. DMRC (2020) Delhi Metro Rail Corporation Ltd. About Us 2020. http://www.delhimetrorail.com/about_us.aspx. Accessed 23 Oct 2020

  38. Delhi Metro Active Routes 2015. https://www.google.com/maps/d/viewer?mid=1mQ86U7IrEiyeYrCx4_dj6kiRKv4&hl=en&ie=UTF8&msa=0&ll=28.601817615509987%2C77.21360904248044&spn=0.309896%2C0.611801&z=12. Accessed 24 Jan 2019

  39. Sharma ML, Singh Y, Gupta SC, Dubey RN, Sen A, Raj D et al (2017) Delhi metro induced vibration measurement on various buildings. Roorkee

  40. Kouroussis G, Verlinden O (2013) Prediction of railway induced ground vibration through multibody and finite element modelling. Mech Sci 4:167–183. https://doi.org/10.5194/ms-4-167-2013

    Article  Google Scholar 

  41. Zhang X, Zhou S, Di H, He C (2018) A semi-analytical model of the train-floating slab track–tunnel–soil system considering the non-linear wheel/rail contact. Proc Inst Mech Eng Part F J Rail Rapid Transit 232:2063–2078. https://doi.org/10.1177/0954409718759879

    Article  Google Scholar 

  42. ABAQUS 2019 (2019)

  43. Kedia NK, Kumar A, Singh Y (2022) Development of empirical relations to predict ground vibrations due to underground metro trains. KSCE J Civ Eng https://doi.org/10.1007/s12205-022-0529-z

  44. Lei X, Noda N-A (2002) Analyses of dynamic response of vehicle and track coupling system with random irregularity of track vertical profile. J Sound Vib 258:147–165. https://doi.org/10.1006/jsvi.2002.5107

    Article  Google Scholar 

  45. Zhang J, Gao Q, Tan SJ, Zhong WX (2012) A precise integration method for solving coupled vehicle–track dynamics with nonlinear wheel–rail contact. J Sound Vib 331:4763–4773. https://doi.org/10.1016/j.jsv.2012.05.033

    Article  Google Scholar 

  46. Lou P (2007) Finite element analysis for train–track–bridge interaction system. Arch Appl Mech 77:707–728. https://doi.org/10.1007/s00419-007-0122-4

    Article  MATH  Google Scholar 

  47. Kedia NK, Kumar A, Singh Y (2021) Effect of rail irregularities and rail pad on track vibration and noise. KSCE J Civ Eng 25:1341–1352. https://doi.org/10.1007/s12205-021-1345-6

    Article  Google Scholar 

  48. Lei X, Wu S, Zhang B (2016) Dynamic analysis of the high speed train and slab track nonlinear coupling system with the cross iteration algorithm. J Nonlinear Dyn 2016:1–17. https://doi.org/10.1155/2016/8356160

    Article  Google Scholar 

  49. Bathe KJ (2014) Finite element procedures, 2nd edn. Watertown

  50. Yang F, Fonder GA (1996) An iterative solution method for dynamic response of bridge-vehicles systems. Earthq Eng Struct Dyn 25:195–215

    Article  Google Scholar 

  51. Sheng X (2019) A review on modelling ground vibrations generated by underground trains. Int J Rail Transp 7:241–261. https://doi.org/10.1080/23248378.2019.1591312

    Article  Google Scholar 

  52. DMRC (2019) Outline design specifications for Phase-IV. New Delhi

  53. Krishna S (2013) Report of the sub-committee on rolling stock for metro railways. New Delhi

  54. Vapnik VN (1998) Statistical learning theory. John Wiley & Sons, Inc., New York

    MATH  Google Scholar 

  55. Khandelwal M (2010) Evaluation and prediction of blast-induced ground vibration using support vector machine. Int J Rock Mech Min Sci 47:509–516. https://doi.org/10.1016/j.ijrmms.2010.01.007

    Article  Google Scholar 

  56. Chen W, Hasanipanah M, Nikafshan Rad H, Jahed Armaghani D, Tahir MM (2021) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng Comput 37:1455–1471. https://doi.org/10.1007/s00366-019-00895-x

    Article  Google Scholar 

  57. Drucker H, Surges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 155–161

  58. Li D, Yan J, Zhang L (2012) Prediction of blast-induced ground vibration using support vector machine by tunnel excavation. Appl Mech Mater 170–173:1414–1418. https://doi.org/10.4028/www.scientific.net/AMM.170-173.1414

    Article  Google Scholar 

  59. Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inf Process Syst 281–287

  60. Ke B, Nguyen H, Bui XN, Costache R (2021) Estimation of ground vibration intensity induced by mine blasting using a state-of-the-art hybrid autoencoder neural network and support vector regression model. Nat Resour Res 30:3853–3864. https://doi.org/10.1007/s11053-021-09890-w

    Article  Google Scholar 

  61. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106. https://doi.org/10.1007/bf00116251

    Article  Google Scholar 

  62. Coimbra R, Rodriguez-Galiano V, Olóriz F, Chica-Olmo M (2014) Regression trees for modeling geochemical data—an application to Late Jurassic carbonates (Ammonitico Rosso). Comput Geosci 73:198–207. https://doi.org/10.1016/j.cageo.2014.09.007

    Article  Google Scholar 

  63. Karbassi A, Mohebi B, Rezaee S, Lestuzzi P (2014) Damage prediction for regular reinforced concrete buildings using the decision tree algorithm. Comput Struct 130:46–56. https://doi.org/10.1016/j.compstruc.2013.10.006

    Article  Google Scholar 

  64. Rana A, Bhagat NK, Jadaun GP, Rukhaiyar S, Pain A, Singh PK (2020) Predicting blast-induced ground vibrations in some Indian tunnels: a comparison of decision tree, artificial neural network and multivariate regression methods. Min Metall Explor 37:1039–1053. https://doi.org/10.1007/s42461-020-00205-w

    Article  Google Scholar 

  65. Sharma M, Singh B, Hemant C (2022) Prediction of backbreak in hot strata/fiery seam of open—pit coal mine by decision tree and random forest algorithm. Arab J Geosci. https://doi.org/10.1007/s12517-022-10627-z

    Article  Google Scholar 

  66. Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316. https://doi.org/10.1007/s00366-016-0475-9

    Article  Google Scholar 

  67. Mcculloch WS, Pitts W (1990) A logical calculus nervous activity. Bull Math Biol 52:99–115

    Article  MATH  Google Scholar 

  68. Shah H, Ghazali R, Nawi NM (2012) Hybrid ant bee colony algorithm for volcano temperature prediction. Commun Comput Inf Sci 281:453–465. https://doi.org/10.1007/978-3-642-28962-0_43

    Article  Google Scholar 

  69. Kamavisdar P, Saluja S, Agrawal S (2013) A survey on image classification approaches and techniques. Int J Adv Res Comput Commun Eng 2:1005–1009

    Google Scholar 

  70. Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Jahed AD (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32:255–266. https://doi.org/10.1007/s00366-015-0415-0

    Article  Google Scholar 

  71. Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67. https://doi.org/10.1016/j.apacoust.2014.01.005

    Article  Google Scholar 

  72. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576. https://doi.org/10.1109/72.97934

    Article  Google Scholar 

  73. Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron Eng 128:785–793. https://doi.org/10.1061/(asce)1090-0241(2002)128:9(785)

    Article  Google Scholar 

  74. Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643. https://doi.org/10.1007/s00521-012-0856-y

    Article  Google Scholar 

  75. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. https://doi.org/10.1016/0893-6080(89)90020-8

    Article  MATH  Google Scholar 

  76. Monjezi M, Amini Khoshalan H, Yazdian VA (2010) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5:441–448. https://doi.org/10.1007/s12517-010-0185-3

    Article  Google Scholar 

  77. Meulenkamp F, Alvarez GM (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39. https://doi.org/10.1016/S0148-9062(98)00173-9

    Article  Google Scholar 

  78. Zhu HT, Huang CZ, Wang J, Lu XY, Feng YX (2008) Machinability of glass by abrasive waterjet. Int J Mater Prod Technol 31:106. https://doi.org/10.1504/IJMPT.2008.015901

    Article  Google Scholar 

  79. Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J. https://doi.org/10.1155/2014/643715

    Article  Google Scholar 

  80. Monjezi M, Ahmadi Z, Varjani AY, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput Appl 23:1101–1107. https://doi.org/10.1007/s00521-012-1038-7

    Article  Google Scholar 

  81. Momeni E, Jahed Armaghani D, Hajihassani M, Mohd Amin MF (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Meas J Int Meas Confed 60:50–63. https://doi.org/10.1016/j.measurement.2014.09.075

    Article  Google Scholar 

  82. Kuo RJ, Wang YC, Tien FC (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18:1161–1170. https://doi.org/10.1016/j.jclepro.2010.03.020

    Article  Google Scholar 

  83. Dreyfus G (2004) Neural networks methodology and applications, 1st edn. Springer, Berlin

    Google Scholar 

  84. Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res (India) 63:32–38

    Google Scholar 

  85. Beale MH, Hagan MT, Demuth HB (2010) Neural network toolbox™ 7 user’s guide, 2010b edn. The MathWorks, Inc., Natick

    Google Scholar 

  86. Negnevitsky M (2005) Artificial intelligence: a guide to intelligent system, 2nd edn. Addison Wesley, Essex

    Google Scholar 

  87. Kennedy J, Eberhart R (2010) Particle swarm optimization. In: Proc. ICNN’95—Int. Conf. neural networks, vol 4. IEEE, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  88. Shi Y, Eberhart R (2007) A modified particle swarm optimizer. In: 1998 IEEE Int. Conf. Evol. Comput. Proceedings. IEEE World Congr. Comput. Intell. (Cat. No.98TH8360). IEEE, pp. 69–73. https://doi.org/10.1109/ICEC.1998.699146

  89. Mezura-Montes E, Coello Coello CA (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1:173–194. https://doi.org/10.1016/j.swevo.2011.10.001

    Article  Google Scholar 

  90. Victoire TAA, Jeyakumar AE (2004) Hybrid PSO-SQP for economic dispatch with valve-point effect. Electr Power Syst Res 71:51–59. https://doi.org/10.1016/j.epsr.2003.12.017

    Article  Google Scholar 

  91. Engelbrecht AP (2007) Computational intelligence: an introduction, 2nd edn. John Wiley & Sons Ltd, West Sussex

    Book  Google Scholar 

  92. Poli R, Kennedy J, Blackwell T (2007) Quantification & Assessment of the chemical form of residual gadolinium in the brain.pdf. Swarm Intell 1:33–57. https://doi.org/10.1007/s11721-007-0002-0

    Article  Google Scholar 

  93. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proc 1999 Congr Evol Comput CEC 1999, vol 3, pp 1945–1950. https://doi.org/10.1109/CEC.1999.785511

  94. Das MT, Dulger LC (2009) Signature verification (SV) toolbox: application of PSO-NN. Eng Appl Artif Intell 22:688–694. https://doi.org/10.1016/j.engappai.2009.02.005

    Article  Google Scholar 

  95. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. Proc IEEE Conf Evol Comput ICEC 1:81–86. https://doi.org/10.1109/cec.2001.934374

    Article  Google Scholar 

  96. Hajihassani M, Jahed Armaghani D, Kalatehjari R (2018) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotech Geol Eng 36:705–722. https://doi.org/10.1007/s10706-017-0356-z

    Article  Google Scholar 

  97. Peng K, Zeng J, Armaghani DJ, Hasanipanah M, Chen Q (2021) A novel combination of gradient boosted tree and optimized ANN models for forecasting ground vibration due to quarry blasting. Nat Resour Res 30:4657–4671. https://doi.org/10.1007/s11053-021-09899-1

    Article  Google Scholar 

  98. Rukhaiyar S, Alam MN, Samadhiya NK (2017) A PSO-ANN hybrid model for predicting factor of safety of slope. Int J Geotech Eng 12:1–11. https://doi.org/10.1080/19386362.2017.1305652

    Article  Google Scholar 

  99. Holland JH (1992) Adaptation in natural And artificial systems. MIT Press, London

    Book  Google Scholar 

  100. Ataei M, Sereshki F (2017) Improved prediction of blast-induced vibrations in limestone mines using genetic algorithm. J Min Environ 8:291–304. https://doi.org/10.22044/jme.2016.654

    Article  Google Scholar 

  101. Yang H, Hasanipanah M, Tahir MM, Bui DT (2020) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res 29:739–750. https://doi.org/10.1007/s11053-019-09515-3

    Article  Google Scholar 

  102. Khandelwal M, Marto A, Fatemi SA, Ghoroqi M, Armaghani DJ, Singh TN et al (2018) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 34:307–317. https://doi.org/10.1007/s00366-017-0541-y

    Article  Google Scholar 

  103. Rashidian V, Hassanlourad M (2013) Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotech Geol Eng 31:1231–1248. https://doi.org/10.1007/s10706-013-9646-2

    Article  Google Scholar 

  104. Chambers LD (2019) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Boca Raton

    Book  Google Scholar 

  105. Armaghani DJ, Hasanipanah M, Mahdiyar A, Abd Majid MZ, Bakhshandeh Amnieh H, Tahir MMD (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29:619–629. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  106. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Meas J Int Meas Confed 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007

    Article  Google Scholar 

  107. Reynaldi A, Lukas S, Margaretha H (2012) Backpropagation and Levenberg–Marquardt algorithm for training finite element neural network. In: Proc—UKSim-AMSS 6th Eur Model Symp EMS 2012, pp 89–94. https://doi.org/10.1109/EMS.2012.56

  108. Paneiro G, Durão FO, Costa e Silva M, Bernardo PA (2020) Neural network approach based on a bilevel optimization for the prediction of underground blast-induced ground vibration amplitudes. Neural Comput Appl 32:5975–5987. https://doi.org/10.1007/s00521-019-04083-2

  109. Chi J, Li X, Wang H, Gao D, Gerstoft P (2019) Sound source ranging using a feed-forward neural network trained with fitting-based early stopping. J Acoust Soc Am 146:EL258–EL264. https://doi.org/10.1121/1.5126115

    Article  Google Scholar 

  110. Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proc. Int. Conf. neural networks. IEEE Press, New York, pp 11–14

  111. Vujičić T, Matijevi T, Ljucović J, Balota A, Ševarac Z (2016) Comparative analysis of methods for determining number of hidden neurons in artificial neural network. In: Cent. Eur. Conf. Inf. Intell. Syst., Varazdin, pp 219–223

  112. Karsoliya S (2012) Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int J Eng Trends Technol 3:714–717

    Google Scholar 

  113. Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. Proc Int Jt Conf Neural Netw 2:1895–1899. https://doi.org/10.1109/ijcnn.2002.1007808

    Article  Google Scholar 

  114. Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32:85–97. https://doi.org/10.1007/s00366-015-0400-7

    Article  Google Scholar 

  115. Mathworks. Particle swarm optimization algorithm 2022. https://in.mathworks.com/help/gads/particle-swarm-optimization-algorithm.html. Accessed 6 May 2022

  116. Mathworks. Genetic algorithm options 2022. https://in.mathworks.com/help/gads/genetic-algorithm-options.html#f14474. Accessed 10 Sept 2022

  117. Avci O, Bhargava A, Nikitas N, Inman DJ (2020) Vibration annoyance assessment of train induced excitations from tunnels embedded in rock. Sci Total Environ 711:1–11. https://doi.org/10.1016/j.scitotenv.2019.134528

    Article  Google Scholar 

Download references

Acknowledgements

The first author received a doctoral fellowship from the Ministry of Education, Government of India, to carry out the research work and is grateful for the same. The author(s) are thankful to the Delhi Metro Rail Corporation Ltd., New Delhi (India), for sharing the necessary information for this research.

Author information

Authors and Affiliations

  1. Centre for Transportation Systems, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Naveen Kumar Kedia

  2. Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Anil Kumar

  3. Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Yogendra Singh

Authors
  1. Naveen Kumar Kedia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yogendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anil Kumar.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper. No funding was received for conducting this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kedia, N.K., Kumar, A. & Singh, Y. Prediction of underground metro train-induced ground vibration using hybrid PSO-ANN approach. Neural Comput & Applic 35, 8171–8195 (2023). https://doi.org/10.1007/s00521-022-08093-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08093-5

Keywords

Search

Navigation