Skip to main content
SpringerLink
Log in

A Machine Learning-Based User-Friendly Approach for Prediction of Traffic-Induced Vibrations and its Application for Parametric Study

  • ORIGINAL CONTRIBUTION
  • Published:
Journal of The Institution of Engineers (India): Series A Aims and scope Submit manuscript

Abstract

The ground-borne vibrations are generated when heavy vehicles travel over the speed bumps at high speeds. Traffic-induced vibrations are annoying for the occupants and damaging for the structures that are present close to the roads. Effective corrective measures are difficult to determine experimentally in cases where the vibrations induced in structures exceed the limits recommended by the governing standard. In this study, a simple approach is created with the help of MS Excel to create a machine learning-based prediction model for traffic-induced vibrations. Additionally, the utility of the developed approach is shown by a parametric investigation for studying the influence of vehicle speed on the vibrations induced by traffic. The Prediction Laboratory add-in tool present in MS Excel is used in this study. Four regression algorithms are selected out of many present in the Prediction Laboratory and coefficient of determination \(\left( {R^{2} } \right)\) is utilized as a performance measuring index for the selection of the best suitable regression algorithm for the available dataset taken from a past study. Compared to the other regression algorithms present in the Machine Learning Laboratory, the decision tree regression algorithm predicted the data points with the greatest coefficient of determination \(\left( {R^{2} } \right)\) value for the available dataset. The parametric investigation showed that the vehicle speed is directly related to the vibration PPV. The parametric trends derived are particular to the problem addressed in the reference study; however, their development by the proposed methodology using limited experimental data will be helpful in setting the speed limits for the vehicles travelling over the roads, keeping in mind the damages associated with the vibrations induced by them in the nearby environment.

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

Similar content being viewed by others

Data Availability

The data will be made available by authors upon request.

References

  1. S.J. Alghamdi, Determining the mix design method for normal strength concrete using machine learning. J. Umm Al-Qura Univ. Eng. Archit. (2023). https://doi.org/10.1007/s43995-023-00022-4

    Article  Google Scholar 

  2. D.J. Armaghani, M. Hajihassani, E.T. Mohamad, A. Marto, S.A. Noorani, Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab. J. Geosci. 7(12), 5383–5396 (2014). https://doi.org/10.1007/s12517-013-1174-0

    Article  Google Scholar 

  3. BS 5228–2, Code of practice for noise and vibration control on construction and open sites–part 2: vibration (2014)

  4. BS 7385–2, Evaluation and measurement for vibration in buildings–part 2: guide to damage levels from groundborne vibration (1993)

  5. D. Chicco, M.J. Warrens, G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 7, 1–24 (2021). https://doi.org/10.7717/PEERJ-CS.623

    Article  Google Scholar 

  6. S. Dadhich, J.K. Sharma, M. Madhira, Prediction of ultimate bearing capacity of aggregate pier reinforced clay using machine learning. Int. J. Geosynth. Ground Eng. 7(2), 1–16 (2021). https://doi.org/10.1007/s40891-021-00282-x

    Article  Google Scholar 

  7. S. Dadhich, J.K. Sharma, M. Madhira, Prediction of Uniaxial compressive strength of rock using machine learning. J. Inst. Eng. India Ser. A 103(4), 1209–1224 (2022). https://doi.org/10.1007/s40030-022-00688-4

    Article  Google Scholar 

  8. DIN 4150–3, Structural vibration–part 3: effects of vibration on structures (1999)

  9. L. Ducarne, D. Ainalis, G. Kouroussis, Assessing the ground vibrations produced by a heavy vehicle traversing a traffic obstacle. Sci. Total Environ. 612, 1568–1576 (2018)

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  11. A. Fişne, C. Kuzu, T. Hüdaverdi, Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ. Monit. Assess. 174(1–4), 461–470 (2011). https://doi.org/10.1007/s10661-010-1470-z

    Article  CAS  PubMed  Google Scholar 

  12. Y. Ghadbhan Abed, T.M. Hasan, R.N. Zehawi, Machine learning algorithms for constructions cost prediction: a systematic review. Int. J. Nonlinear Anal. Appl. 13, 2008–6822 (2022). https://doi.org/10.22075/ijnaa.2022.27673.3684

    Article  Google Scholar 

  13. C. Hall, K. Beer, J. Robertson, T. Nguyen, F. Zafar, T. Tan, A. Mani, T. Beer, Guide to Road Safety Part 3: Safe Speed (Austroads, Sydney, 2021)

    Google Scholar 

  14. A.F. Ibrahim, A. Abdelaal, S. Elkatatny, Formation resistivity prediction using decision tree and random forest. Arab. J. Sci. Eng. 47(9), 12183–12191 (2022). https://doi.org/10.1007/s13369-022-06900-8

    Article  CAS  Google Scholar 

  15. M. Iphar, M. Yavuz, H. Ak, Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ. Geol. 56(1), 97–107 (2008). https://doi.org/10.1007/s00254-007-1143-6

    Article  ADS  Google Scholar 

  16. A. Jakubczyk-Gałczyńska, Predicting the impact of traffic-induced vibrations on buildings using artificial neural networks. MATEC Web of Conferences, vol 219 (2018). https://doi.org/10.1051/matecconf/201821904004

  17. A. Jakubczyk-Gałczyńska, R. Jankowski, A proposed machine learning model for forecasting impact of traffic-induced vibrations on buildings. LNCS 12139, 444–451 (2020). https://doi.org/10.1007/978-3-030-50420-5_33

    Article  Google Scholar 

  18. M.F. Javaid, R. Azam, M.R. Riaz, Decision tree regression algorithm-based model for prediction of traffic-induced vibrations: development and application for parametric investigation. Innov. Infrastruct. Solut. 8(3), 103 (2023)

    Article  Google Scholar 

  19. S.U. Kim, K.S. Lee, Regional low flow frequency analysis using Bayesian regression and prediction at ungauged catchment in Korea. KSCE J. Civ. Eng. 14(1), 87–98 (2010). https://doi.org/10.1007/s12205-010-0087-7

    Article  Google Scholar 

  20. J.A. Lahausse, N. van Nes, B.N. Fildes, M.D. Keall, Attitudes towards current and lowered speed limits in Australia. Accid. Anal. Prev. 42(6), 2108–2116 (2010). https://doi.org/10.1016/j.aap.2010.06.024

    Article  PubMed  Google Scholar 

  21. X. Li, Z. Jiang, S. Wang, X. Li, Y. Liu, X. Wang, A study of a model for predicting pneumatic subsoiling resistance based on machine learning techniques. Agronomy 13(4), 1079 (2023). https://doi.org/10.3390/agronomy13041079

    Article  Google Scholar 

  22. M. Liang, E.T. Mohamad, R.S. Faradonbeh, D. Jahed Armaghani, S. Ghoraba, Rock strength assessment based on regression tree technique. Eng. Comput. 32(2), 343–354 (2016). https://doi.org/10.1007/s00366-015-0429-7

    Article  Google Scholar 

  23. G. Lombaert, G. Degrande, Experimental validation of a numerical prediction model for free field traffic induced vibrations by in situ experiments. Soil Dyn. Earthq. Eng. 21, 485–497 (2001)

    Article  Google Scholar 

  24. G. Lombaert, G. Degrande, The experimental validation of a numerical model for the prediction of the vibrations in the free field produced by road traffic. J. Sound Vib. 262, 309–331 (2003)

    Article  ADS  Google Scholar 

  25. G. Loterman, C. Mues, Learning algorithm selection for comprehensible regression analysis using datasetoids. Intell. Data Anal. 19(5), 1019–1034 (2015)

    Article  Google Scholar 

  26. R. Malhotra, Comparative analysis of statistical and machine learning methods for predicting faulty modules. Appl. Soft Comput. J. 21, 286–297 (2014). https://doi.org/10.1016/j.asoc.2014.03.032

    Article  Google Scholar 

  27. M. Mhanna, M. Sadek, I. Shahrour, Numerical modeling of traffic-induced ground vibration. Comput. Geotech. 39, 116–123 (2012). https://doi.org/10.1016/j.compgeo.2011.07.005

    Article  Google Scholar 

  28. NCHRP, Current practices to address construction vibration and potential effects to historic buildings adjacent to transportation projects (2012)

  29. C.K. Oh, J.L. Beck, A bayesian learning method for structural damage assessment of phase I IASC-ASCE benchmark problem. KSCE J. Civ. Eng. 22(3), 987–992 (2018). https://doi.org/10.1007/s12205-018-1290-1

    Article  Google Scholar 

  30. A. Rahnama, G. Zepon, S. Sridhar, Machine learning based prediction of metal hydrides for hydrogen storage, part I: prediction of hydrogen weight percent. Int. J. Hydrogen Energy 44(14), 7337–7344 (2019). https://doi.org/10.1016/j.ijhydene.2019.01.261

    Article  CAS  Google Scholar 

  31. J.A. Sanchidrián, Flyrock prediction by multiple regression analysis in Esfordi phosphate mine of Iran (2010)

  32. M.S. Sandeep, K. Tiprak, S. Kaewunruen, P. Pheinsusom, W. Pansuk, Shear strength prediction of reinforced concrete beams using machine learning. Structures 47, 1196–1211 (2023)

    Article  Google Scholar 

  33. A. Saracoglu, H. Ozen, Estimation of traffic incident duration: a comparative study of decision tree models. Arab. J. Sci. Eng. 45(10), 8099–8110 (2020). https://doi.org/10.1007/s13369-020-04615-2

    Article  Google Scholar 

  34. C.M. Shakya, R.K. Bhattacharjya, S. Dadhich, Groundwater level prediction with machine learning for the Vidisha district, a semi-arid region of Central India. Groundw. Sustain. Dev. 19, 100825 (2022)

    Article  Google Scholar 

  35. G. Shanmugasundar, M. Vanitha, R. Čep, V. Kumar, K. Kalita, M. Ramachandran, A comparative study of linear, random forest and adaboost regressions for modeling non-traditional machining. Processes 9(11), 2015 (2021). https://doi.org/10.3390/pr9112015

    Article  CAS  Google Scholar 

  36. S.A. Shourehdeli, K. Mobini, Machine learning-based models for frictional pressure drop prediction of condensing and adiabatic flow in micro, mini and macro channels utilizing universal data. Int. J. Air Cond. Refrig. 31(1), 8 (2023). https://doi.org/10.1007/s44189-023-00025-9

    Article  Google Scholar 

  37. B.V. Varma, E.V. Prasad, S. Singha, Study on predicting compressive strength of concrete using supervised machine learning techniques. Asian J. Civ. Eng. (2023). https://doi.org/10.1007/s42107-023-00662-w

    Article  Google Scholar 

  38. G.R. Watts, V.V. Krylov, Ground-borne vibration generated by vehicles crossing road humps and speed control cushions. Appl. Acoust. 59(3), 221–236 (2000). https://doi.org/10.1016/S0003-682X(99)00026-2

    Article  Google Scholar 

  39. A.C. Whiffin, D. Leonard, Survey of traffic-induced vibrations, in Engineering. (The National Archives, Kew, 1971)

    Google Scholar 

  40. Z. Xu, Y. Lou, L. Chen, In situ experiment and analysis of the attenuation characteristics of environmental vibrations based on frequency sweep method. IOP Conf. Ser. Earth Environ. Sci. 555(1), 012103 (2020). https://doi.org/10.1088/1755-1315/555/1/012103

    Article  Google Scholar 

  41. Z. Xu, M. Ma, Z. Zhou, X. Xie, H. Xie, B. Jiang, Z. Zhang, Prediction of metro train-induced tunnel vibrations using machine learning method. Adv. Civ. Eng. 2022, 1–10 (2022). https://doi.org/10.1155/2022/4031050

    Article  Google Scholar 

  42. Z. Zarei, J. Sadeghi, A. Sarkar, Evaluation of heavy-vehicle-induced vibrations running on asphalt pavements. Constr. Build. Mater. 358, 129399 (2022)

    Article  Google Scholar 

  43. C. Zhang, N. Zhang, Y. Zhang, X. Liu, Prediction of traffic vibration environment of ancient wooden structures based on the response transfer ratio function. Sensors 22(21), 8414 (2022). https://doi.org/10.3390/s22218414

    Article  ADS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Higher Education Commission, Pakistan NRPU Project (No. 15954).

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan

    Muhammad Faraz Javaid, Rizwan Azam & Muhammad Rizwan Riaz

  2. Mining Engineering Department, University of Engineering and Technology, Lahore, 54890, Pakistan

    Shahab Saqib

Authors
  1. Muhammad Faraz Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rizwan Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahab Saqib
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Rizwan Riaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to Muhammad Rizwan Riaz.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

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

Javaid, M.F., Azam, R., Saqib, S. et al. A Machine Learning-Based User-Friendly Approach for Prediction of Traffic-Induced Vibrations and its Application for Parametric Study. J. Inst. Eng. India Ser. A 105, 1–13 (2024). https://doi.org/10.1007/s40030-023-00775-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40030-023-00775-0

Keywords

Search

Navigation