SpringerLink
Log in

Predicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning Algorithms

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Pipeline construction projects are necessary to provide gas and liquid energy transportation. Although various studies have investigated the contributing factors to accidents occurring in pipeline construction projects, there is a need for a predictive model for such incidents. The main purpose of this study is to provide an artificial intelligence-based model to predict the outcomes of occupational accidents. In this context, 1184 incident cases, including injury, near-miss, and asset-product damage, taken from a pipeline construction project constituted the primary dataset. Twelve prediction models are formed by changing the input domain according to the type of incident, time, and cause type attributes, leading to 12 distinct sub-datasets. Then, each dataset is tested with 11 different machine learning (ML) algorithms to derive an effective prescriptive model. The descriptive results show that low awareness of job hazards and improper vehicle operations were the most critical immediate causes, while failure in risk recognition and site supervision were the major root causes of pipeline construction accidents. Among the ML methods, the deep learning algorithm performed better than its counterparts in eight sub-datasets. Finally, a prescriptive model incorporating the ML application procedure is recommended for construction companies to reduce occupational accidents. Overall, the proposed model and findings are expected to contribute to preventing and reducing construction accidents in pipeline projects by adopting relevant strategies.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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

(Adapted from Lepenioti et al. 2020)

Similar content being viewed by others

Data Availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

AUC:

Area under curve

BN:

Bayesian network

CT:

Classification tree

DL:

Deep learning

DT:

Decision tree

ELM:

Extreme learning machine

GA:

Genetic algorithm

GLM:

Generalized linear model

IC:

Immediate cause

IT:

Information technology

KNN:

K-nearest neighbor

LFM:

Large fast margin

LGR:

Logistic regression

LR:

Linear regression

ML:

Machine learning

NB:

Naïve bayes

NLP:

Natural language processing

OHS:

Occupational health and safety

RC:

Root cause

RF:

Random forest

ROC:

Receiver operating characteristic

SGTB:

Stochastic gradient tree boosting

SVM:

Support vector machine

References

  1. Choi, J.; Gu, B.; Chin, S.; Lee, J.S.: Machine learning predictive model based on national data for fatal accidents of construction workers. Autom. Constr. (2020). https://doi.org/10.1016/j.autcon.2019.102974

    Article  Google Scholar 

  2. Koc, K.; Gurgun, A.P.: Scenario-based automated data preprocessing to predict severity of construction accidents. Autom. Constr. 140, 104351 (2022). https://doi.org/10.1016/j.autcon.2022.104351

    Article  Google Scholar 

  3. Tixier, A.J.P.; Hallowell, M.R.; Rajagopalan, B.; Bowman, D.: Application of machine learning to construction injury prediction. Autom. Constr. (2016). https://doi.org/10.1016/j.autcon.2016.05.016

    Article  Google Scholar 

  4. Bureau of Labor Statistics, National Census Of Fatal Occupational Injuries In 2019, 2019. https://www.bls.gov/news.release/pdf/cfoi.pdf.

  5. Kang, K.; Ryu, H.: Predicting types of occupational accidents at construction sites in Korea using random forest model. Saf. Sci. 120, 226–236 (2019). https://doi.org/10.1016/j.ssci.2019.06.034

    Article  Google Scholar 

  6. Shin, D.P.; Park, Y.J.; Seo, J.; Lee, D.E.: Association Rules Mined from Construction Accident Data. KSCE J. Civ. Eng. (2018). https://doi.org/10.1007/s12205-017-0537-6

    Article  Google Scholar 

  7. Jabbari, M.; Yousefpour, Y.; Ghaffari, M.; Shokuhian, A.: Evaluation of effectiveness of risk-based comprehensive safety training planning in the gas pipeline construction industry. Int. J. Occup. Saf. Ergon. 28, 2468–2481 (2021). https://doi.org/10.1080/10803548.2021.2002584

    Article  Google Scholar 

  8. Mete, S.: Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment. Hum. Ecol. Risk Assess. (2019). https://doi.org/10.1080/10807039.2018.1546115

    Article  Google Scholar 

  9. Ekmekcioğlu, Ö.; Koc, K.: Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards. CATENA 216, 106379 (2022). https://doi.org/10.1016/j.catena.2022.106379

    Article  Google Scholar 

  10. Koopialipoor, M.; Asteris, P.G.; Salih Mohammed, A.; Alexakis, D.E.; Mamou, A.; Armaghani, D.J.: Introducing stacking machine learning approaches for the prediction of rock deformation. Transp. Geotech. 34, 100756 (2022). https://doi.org/10.1016/j.trgeo.2022.100756

    Article  Google Scholar 

  11. Lim, C.S.; Mohamad, E.T.; Motahari, M.R.; Armaghani, D.J.; Saad, R.: Machine learning classifiers for modeling soil characteristics by geophysics investigations: a comparative study. Appl. Sci. (2020). https://doi.org/10.3390/APP10175734

    Article  Google Scholar 

  12. Asteris, P.G.; Koopialipoor, M.; Armaghani, D.J.; Kotsonis, E.A.; Lourenço, P.B.: Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput. Appl. 33(19), 1–33 (2021). https://doi.org/10.1007/s00521-021-06004-8

    Article  Google Scholar 

  13. Indraratna, B.; Armaghani, D.J.; Gomes Correia, A.; Hunt, H.; Ngo, T.: Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques. Transp. Geotech. 38, 100895 (2023). https://doi.org/10.1016/j.trgeo.2022.100895

    Article  Google Scholar 

  14. Faizi, K.; Armaghani, D.J.; Momeni, E.; Nazir, R.; Mohamad, E.T.: Uplift resistance of buried pipelines enhanced by geogrid. Soil Mech. Found. Eng. 51, 188–195 (2014). https://doi.org/10.1007/s11204-014-9276-6

    Article  Google Scholar 

  15. Koopialipoor, M.; Tootoonchi, H.; Marto, A.; Faizi, K.; Armaghani, D.J.: Various effective factors on peak uplift resistance of pipelines in sand: a comparative study. Int. J. Geotech. Eng. 14, 820–827 (2020). https://doi.org/10.1080/19386362.2018.1482987

    Article  Google Scholar 

  16. Zeng, J.; Asteris, P.G.; Mamou, A.P.; Mohammed, A.S.; Golias, E.A.; Armaghani, D.J.; Faizi, K.; Hasanipanah, M.: The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand. Appl. Sci. 11, 1–18 (2021). https://doi.org/10.3390/app11030908

    Article  Google Scholar 

  17. Poh, C.Q.X.; Ubeynarayana, C.U.; Goh, Y.M.: Safety leading indicators for construction sites: a machine learning approach. Autom. Constr. 93, 375–386 (2018). https://doi.org/10.1016/j.autcon.2018.03.022

    Article  Google Scholar 

  18. Sarkar, S.; Maiti, J.: Machine learning in occupational accident analysis: a review using science map** approach with citation network analysis. Saf. Sci. 131, 104900 (2020). https://doi.org/10.1016/j.ssci.2020.104900

    Article  Google Scholar 

  19. Koc, K.; Ekmekcioğlu, Ö.; Gurgun, A.P.: Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Autom. Constr. (2021). https://doi.org/10.1016/j.autcon.2021.103896

    Article  Google Scholar 

  20. Baker, H.; Hallowell, M.R.; Tixier, A.J.P.: AI-based prediction of independent construction safety outcomes from universal attributes. Autom. Constr. (2020). https://doi.org/10.1016/j.autcon.2020.103146

    Article  Google Scholar 

  21. Ayhan, B.U.; Tokdemir, O.B.: Accident analysis for construction safety using latent class clustering and artificial neural networks. J. Constr. Eng. Manag. (2020). https://doi.org/10.1061/(asce)co.1943-7862.0001762

    Article  Google Scholar 

  22. Mistikoglu, G.; Gerek, I.H.; Erdis, E.; Mumtaz; Usmen, P.E.; Cakan, H.; Kazan, E.E.: Decision tree analysis of construction fall accidents involving roofers. Expert Syst. Appl. (2015). https://doi.org/10.1016/j.eswa.2014.10.009

    Article  Google Scholar 

  23. Ajayi, A.; Oyedele, L.; Owolabi, H.; Akinade, O.; Bilal, M.; Davila Delgado, J.M.; Akanbi, L.: Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Anal. (2020). https://doi.org/10.1111/risa.13425

    Article  Google Scholar 

  24. Artanova, M.V.; Ivanova, M.V.; Korobov, A.V.: Risk-oriented approach to industrial control over occupational health & safety at the main gas pipeline construction Stage. IOP Conf. Ser. Mater. Sci. Eng. 1079, 062071 (2021). https://doi.org/10.1088/1757-899x/1079/6/062071

    Article  Google Scholar 

  25. Pettitt, G.; Pennicott, P.: Use of bowties for pipeline safety management. Proc. Bienn. Int. Pipeline Conf. IPC (2016). https://doi.org/10.1115/IPC2016-64243

    Article  Google Scholar 

  26. Sun, D.; Xu, F.; He, H.: Safety risk analysis for gas pipeline construction. In: ICPTT 2012 Better Pipeline Infrastructure a Better Life—Proceedings of the International Conference on Pipelines and Trenchless Technology 2012, pp 1258–1263 (2013) https://doi.org/10.1061/9780784412619.128

  27. Goh, Y.M.; Ubeynarayana, C.U.: Construction accident narrative classification: an evaluation of text mining techniques. Accid. Anal. Prev. (2017). https://doi.org/10.1016/j.aap.2017.08.026

    Article  Google Scholar 

  28. Liu, M.; Chong, H.Y.; Liao, P.C.: A novel approach based on fluid dynamics for on-site safety assessment. KSCE J. Civ. Eng. (2021). https://doi.org/10.1007/s12205-021-1027-4

    Article  Google Scholar 

  29. **e, B.; Xu, J.; Jung, J.; Yun, S.H.; Zeng, E.; Brooks, E.M.; Dolk, M.; Narasimhalu, L.: Machine learning on satellite radar images to estimate damages after natural disasters. In: GIS Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (2020) https://doi.org/10.1145/3397536.3422349.

  30. Rizzo, F.; Caracoglia, L.: Examination of artificial neural networks to predict wind-induced displacements of cable net roofs. Eng. Struct. 245, 112956 (2021). https://doi.org/10.1016/j.engstruct.2021.112956

    Article  Google Scholar 

  31. Hwang, S.H.; Mangalathu, S.; Shin, J.; Jeon, J.S.: Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames. J. Build. Eng. 34, 101905 (2021). https://doi.org/10.1016/j.jobe.2020.101905

    Article  Google Scholar 

  32. Cho, C.; Kim, K.; Park, J.; Cho, Y.K.: Data-driven monitoring system for preventing the collapse of scaffolding structures. J. Constr. Eng. Manag. 144, 1–12 (2018). https://doi.org/10.1061/(asce)co.1943-7862.0001535

    Article  Google Scholar 

  33. Matías, J.M.; Rivas, T.; Martín, J.E.; Taboada, J.: A machine learning methodology for the analysis of workplace accidents. Int. J. Comput. Math. 85, 559–578 (2008). https://doi.org/10.1080/00207160701297346

    Article  MathSciNet  MATH  Google Scholar 

  34. Ayhan, B.U.; Tokdemir, O.B.: Predicting the outcome of construction incidents. Saf. Sci. 113, 91–104 (2019). https://doi.org/10.1016/j.ssci.2018.11.001

    Article  Google Scholar 

  35. Wang, W.; Liu, X.; Ma, Y.; Liu, S.: A new approach for occupational risk evaluation of natural gas pipeline construction with extended cumulative prospect theory. Int. J. Fuzzy Syst. 23, 158–181 (2021). https://doi.org/10.1007/s40815-020-01038-x

    Article  Google Scholar 

  36. Mubin, S.; Mubin, G.: Risk analysis for construction and operation of gas pipeline projects in Pakistan. Pak. J. Eng. Appl. Sci. 2, 12–19 (2008)

    Google Scholar 

  37. Ramírez-Camacho, J.G.; Carbone, F.; Pastor, E.; Bubbico, R.; Casal, J.: Assessing the consequences of pipeline accidents to support land-use planning. Saf. Sci. 97, 34–42 (2017). https://doi.org/10.1016/j.ssci.2016.01.021

    Article  Google Scholar 

  38. Peng, X.Y.; Yao, D.C.; Liang, G.C.; Yu, J.S.; He, S.: Overall reliability analysis on oil/gas pipeline under typical third-party actions based on fragility theory. J. Nat. Gas Sci. Eng. 34, 993–1003 (2016). https://doi.org/10.1016/j.jngse.2016.07.060

    Article  Google Scholar 

  39. Kraidi, L.; Shah, R.; Matipa, W.; Borthwick, F.: Analyzing stakeholders’ perceptions of the critical risk factors in oil and gas pipeline projects. Period. Polytech. Archit. (2019). https://doi.org/10.3311/ppar.13744

    Article  Google Scholar 

  40. Jahed, A.; Nikoomaram, H.; Ghaffari, F.: Analyzing the factors influencing occurrence of occupational accidents in fly-in/fly-out workers of a gas pipeline dispatching project. J. Heal. Saf. Work 10, 9–12 (2020)

    Google Scholar 

  41. Lepenioti, K.; Bousdekis, A.; Apostolou, D.; Mentzas, G.: Prescriptive analytics: literature review and research challenges. Int. J. Inf. Manag. 50, 57–70 (2020). https://doi.org/10.1016/j.i**fomgt.2019.04.003

    Article  Google Scholar 

  42. Bureau of Labor Statistics: Occupational Injury and Illness Classification Manual, U.S. Department of Labor, pp. 1–353 (2007)

  43. Hegde, J.; Rokseth, B.: Applications of machine learning methods for engineering risk assessment—a review. Saf. Sci. 122, 104492 (2020). https://doi.org/10.1016/j.ssci.2019.09.015

    Article  Google Scholar 

  44. Mierswa, I.; Klinkenberg, R.: RapidMiner Studio (9.2) [Data science, machine learning, predictive analytics] (2018)

  45. Alsakka, F.; Mohamed, Y.; Al-Hussein, M.: Evaluation and comparison of the performance of artficial intelligence algorithms in predicting construction safety incidents. In: 38th International Symposium Automation Robotics Construction (2021). https://doi.org/10.22260/isarc2021/0077

  46. Phark, C.; Kim, W.; Yoon, Y.S.; Shin, G.; Jung, S.: Prediction of issuance of emergency evacuation orders for chemical accidents using machine learning algorithm. J. Loss. Prev. Process Ind. 56, 162–169 (2018). https://doi.org/10.1016/j.jlp.2018.08.021

    Article  Google Scholar 

  47. Ali, E.M.; Ahmed, M.M.; Wulff, S.S.: Detection of critical safety events on freeways in clear and rainy weather using SHRP2 naturalistic driving data: parametric and non-parametric techniques. Saf. Sci. 119, 141–149 (2019). https://doi.org/10.1016/j.ssci.2019.01.007

    Article  Google Scholar 

  48. Beunza, J.J.; Puertas, E.; García-Ovejero, E.; Villalba, G.; Condes, E.; Koleva, G.; Hurtado, C.; Landecho, M.F.: Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J. Biomed. Inform. (2019). https://doi.org/10.1016/j.jbi.2019.103257

    Article  Google Scholar 

  49. Zhu, W.; Zeng, N.; Wang, N.: Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations., Northeast SAS Users Gr. 2010 Heal. Care Life Science, pp. 1–9 (2010)

  50. Hosmer, D.W.; Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2005) https://doi.org/10.1002/0471722146

    Book  MATH  Google Scholar 

  51. Landis, J.R.; Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159 (1977). https://doi.org/10.2307/2529310

    Article  MATH  Google Scholar 

  52. Kazaz, A.; Ulubeyli, S.: Drivers of productivity among construction workers: a study in a develo** country. Build. Environ. 42, 2132–2140 (2007). https://doi.org/10.1016/j.buildenv.2006.04.020

    Article  Google Scholar 

  53. Arifuddin, R.; Suraji, A.; Latief, Y.: Study of the causal factors of construction projects vulnerability to accidents. Int. J. Innov. Technol. Explor. Eng. 8, 711–716 (2019)

    Google Scholar 

  54. Haslam, R.A.; Hide, S.A.; Gibb, A.G.F.; Gyi, D.E.; Pavitt, T.; Atkinson, S.; Duff, A.R.: Contributing factors in construction accidents. Appl. Ergon. (2005). https://doi.org/10.1016/j.apergo.2004.12.002

    Article  Google Scholar 

  55. Wilkins, J.R.: Construction workers’ perceptions of health and safety training programmes. Constr. Manag. Econ. 29, 1017–1026 (2011). https://doi.org/10.1080/01446193.2011.633538

    Article  Google Scholar 

  56. Gurgun, A.P.; Koc, K.; Kunkcu, H.: Exploring the adoption of technology against delays in construction projects. Eng. Constr. Archit. Manag. (2022). https://doi.org/10.1108/ECAM-06-2022-0566

    Article  Google Scholar 

  57. Xu, Q.; Xu, K.: Analysis of the characteristics of fatal accidents in the construction industry in china based on statistical data. Int. J. Environ. Res. Public Health. (2021). https://doi.org/10.3390/ijerph18042162

    Article  Google Scholar 

  58. Zhu, Y.; Zhou, J.; Zhang, B.; Wang, H.; Huang, M.: Statistical analysis of major tunnel construction accidents in China from 2010 to 2020. Tunn. Undergr. Sp. Technol. (2022). https://doi.org/10.1016/j.tust.2022.104460

    Article  Google Scholar 

  59. Amiri, M.; Ardeshir, A.; Fazel Zarandi, M.H.: Risk-based analysis of construction accidents in Iran during 2007–2011-meta analyze study. Iran J. Public Health 43(4), 507–522 (2014)

    Google Scholar 

  60. Harvey, E.J.; Waterson, P.; Dainty, A.R.J.: Beyond ConCA: rethinking causality and construction accidents. Appl. Ergon. 73, 108–121 (2018). https://doi.org/10.1016/j.apergo.2018.06.001

    Article  Google Scholar 

  61. Hinze, J.W.; Teizer, J.: Visibility-related fatalities related to construction equipment. Saf. Sci. 49, 709–718 (2011). https://doi.org/10.1016/j.ssci.2011.01.007

    Article  Google Scholar 

  62. Kang, Y.; Siddiqui, S.; Suk, S.J.; Chi, S.; Kim, C.: Trends of fall accidents in the construction, industry U.S. J. Constr. Eng. Manag. 143, 04017043 (2017). https://doi.org/10.1061/(asce)co.1943-7862.0001332

    Article  Google Scholar 

  63. Shao, B.; Hu, Z.; Liu, Q.; Chen, S.; He, W.: Fatal accident patterns of building construction activities in China. Saf. Sci. (2019). https://doi.org/10.1016/j.ssci.2018.07.019

    Article  Google Scholar 

  64. Zhang, L.; Tan, J.; Han, D.; Zhu, H.: From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22, 1680–1685 (2017). https://doi.org/10.1016/j.drudis.2017.08.010

    Article  Google Scholar 

  65. Zhu, R.; Hu, X.; Hou, J.; Li, X.: Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Saf. Environ. Prot. (2021). https://doi.org/10.1016/j.psep.2020.08.006

    Article  Google Scholar 

  66. Larson, D.; Chang, V.: A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manag. 36, 700–710 (2016). https://doi.org/10.1016/j.i**fomgt.2016.04.013

    Article  Google Scholar 

  67. Šikšnys, L.; Pedersen, T.B.: Prescriptive analytics. In: Encyclopedia of Database Systems, pp. 1–2 (2016). https://doi.org/10.1007/978-1-4899-7993-3_80624-1.

  68. Krumeich, J.; Werth, D.; Loos, P.: Prescriptive control of business processes: new potentials through predictive analytics of big data in the process manufacturing industry. Bus. Inf. Syst. Eng. 58, 261–280 (2016). https://doi.org/10.1007/s12599-015-0412-2

    Article  Google Scholar 

  69. Engel, Y.; Etzion, O.: Towards proactive event-driven computing. In: DEBS’11—Proceedings of the 5th ACM International Conference on Distributed Event-Based Systems, pp. 125–136 (2011). https://doi.org/10.1145/2002259.2002279.

  70. Engel, Y.; Etzion, O.; Feldman, Z.; A basic model for proactive event-driven computing. In: Proceedings 6th ACM International Conference on Distributed and Event-based Systems DEBS’12, pp. 107–118 (2012). https://doi.org/10.1145/2335484.2335496.

Download references

Acknowledgements

The authors would like to thank the executives who anonymously provided the data and OHS experts who participated in the feedback process for their efforts and contribution.

Author information

Authors and Affiliations

  1. Occupational Health and Safety Program, Graduate School of Natural and Applied Sciences, Middle East Technical Univ., 06800, Ankara, Turkey

    Ahmad Mammadov

  2. Department of Civil Engineering, Istanbul Gelisim University, 34310, Avcilar, Istanbul, Turkey

    Gokhan Kazar

  3. Department of Civil Engineering, Yildiz Technical University, 34220, Esenler, Istanbul, Turkey

    Kerim Koc

  4. Department of Civil Engineering, Istanbul Technical University, 34469, Maslak, Istanbul, Turkey

    Onur Behzat Tokdemir

Authors
  1. Ahmad Mammadov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gokhan Kazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kerim Koc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Onur Behzat Tokdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Onur Behzat Tokdemir.

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

Mammadov, A., Kazar, G., Koc, K. et al. Predicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning Algorithms. Arab J Sci Eng 48, 13771–13789 (2023). https://doi.org/10.1007/s13369-023-07964-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-07964-w

Keywords

Search

Navigation