Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 363

The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.

Applications of machine learning methods for engineering risk assessment – A review

Open access
Hegde, Jeevith, Rokseth, Børge·2020·Safety Science
DOI 10.1016/j.ssci.2019.09.015
Open this paper in CoChat — free account

Abstract

The purpose of this article is to present a structured review of publications utilizing machine learning methods to aid in engineering risk assessment. A keyword search is performed to retrieve relevant articles from the databases of Scopus and Engineering Village. The search results are filtered according to seven selection criteria. The filtering process resulted in the retrieval of one hundred and twenty-four relevant research articles. Statistics based on different categories from the citation database is presented. By reviewing the articles, additional categories, such as the type of machine learning algorithm used, the type of input source used, the type of industry targeted, the type of implementation, and the intended risk assessment phase are also determined. The findings show that the automotive industry is leading the adoption of machine learning algorithms for risk assessment. Artificial neural networks are the most applied machine learning method to aid in engineering risk assessment. Additional findings from the review process are also presented in this article.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ssci.2019.09.015

Cites

97 of 97 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 97 references

Research works printed but not linked: 40

  1. Anghel, 2009, Risk assessment for pipelines with active defects based on artificial intelligence methods, Int. J. Press. Vessel. Pip., 86, 403, 10.1016/j.ijpvp.2009.01.009 · doi:10.1016/j.ijpvp.2009.01.009
  2. Ayo-Imoru, 2018, Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant, Ann. Nucl. Energy, 118, 61, 10.1016/j.anucene.2018.04.002 · doi:10.1016/j.anucene.2018.04.002
  3. Bevilacqua, 2010, Data mining for occupational injury risk: A case study, Int. J. Reliab. Qual. Saf. Eng., 17, 351, 10.1142/S021853931000386X · doi:10.1142/s021853931000386x
  4. Bukharov, 2015, Development of a decision support system based on neural networks and a genetic algorithm, Expert Syst. Appl., 42, 6177, 10.1016/j.eswa.2015.03.018 · doi:10.1016/j.eswa.2015.03.018
  5. Castro, 2016, Data mining on road safety: Factor assessment on vehicle accidents using classification models, Int. J. Crashworthiness, 21, 104, 10.1080/13588265.2015.1122278 · doi:10.1080/13588265.2015.1122278
  6. Chen, 2015, Data mining on Chinese train accidents to derive associated rules, Proc. Inst. Mech. Eng. Part F J. Rail Rapid. Transit., 231, 239, 10.1177/0954409715624724 · doi:10.1177/0954409715624724
  7. Christopher, 2014, Prediction of warning level in aircraft accidents using data mining techniques, Aeronaut. J., 118, 935, 10.1017/S0001924000009623 · doi:10.1017/s0001924000009623
  8. Curiel-Ramirez, 2018, Towards of a modular framework for semi-autonomous driving assistance systems, Int. J. Interact. Des. Manuf.
  9. Ding, 2013, Development of web-based system for safety risk early warning in urban metro construction, Autom. Constr., 34, 45, 10.1016/j.autcon.2012.11.001 · doi:10.1016/j.autcon.2012.11.001
  10. Ding, 2017, The safety management of urban rail transit based on operation fault log, Saf. Sci., 94, 10, 10.1016/j.ssci.2016.12.015 · doi:10.1016/j.ssci.2016.12.015
  11. Farid, 2019, Comparative analysis of multiple techniques for developing and transferring safety performance functions, Accid. Anal. Prev., 122, 85, 10.1016/j.aap.2018.09.024 · doi:10.1016/j.aap.2018.09.024
  12. Farid, 2018, A new approach for calibrating safety performance functions, Accid. Anal. Prev., 119, 188, 10.1016/j.aap.2018.07.023 · doi:10.1016/j.aap.2018.07.023
  13. Fink, 2014, Predicting component reliability and level of degradation with complex-valued neural networks, Reliab. Eng. Syst. Saf., 121, 198, 10.1016/j.ress.2013.08.004 · doi:10.1016/j.ress.2013.08.004
  14. Gernand, 2016, Evaluating the effectiveness of mine safety enforcement actions in forecasting the lost-days rate at specific worksites, ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng., 2
  15. Gnecco, 2017, Supervised and semi-supervised classifiers for the detection of flood-prone areas, Soft Comput., 21, 3673, 10.1007/s00500-015-1983-z · doi:10.1007/s00500-015-1983-z
  16. Kaeeni, 2018, Derailment accident risk assessment based on ensemble classification method, Saf. Sci., 110, 3, 10.1016/j.ssci.2017.11.006 · doi:10.1016/j.ssci.2017.11.006
  17. Krkač, 2017, Method for prediction of landslide movements based on random forests, Landslides, 14, 947, 10.1007/s10346-016-0761-z · doi:10.1007/s10346-016-0761-z
  18. Mandelli, 2018, Mining data in a dynamic PRA framework, Prog. Nucl. Energy, 108, 99, 10.1016/j.pnucene.2018.05.004 · doi:10.1016/j.pnucene.2018.05.004
  19. Marucci-Wellman, 2017, Classifying injury narratives of large administrative databases for surveillance—A practical approach combining machine learning ensembles and human review, Accid. Anal. Prev., 98, 359, 10.1016/j.aap.2016.10.014 · DOI
  20. Mirabadi, 2010, Application of association rules in Iranian Railways (RAI) accident data analysis, Saf. Sci., 48, 1427, 10.1016/j.ssci.2010.06.006 · doi:10.1016/j.ssci.2010.06.006
  21. Park, 2018, Apriori-based text mining method for the advancement of the transportation management plan in expressway work zones, J. Supercomput., 74, 1283, 10.1007/s11227-017-2142-3 · doi:10.1007/s11227-017-2142-3
  22. Saha, 2016, A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection, J. Adv. Transp., 50, 522, 10.1002/atr.1358 · doi:10.1002/atr.1358
  23. Samui, 2013, The use of a relevance vector machine in predicting liquefaction potential, Indian Geotech. J., 44, 458, 10.1007/s40098-013-0094-y · doi:10.1007/s40098-013-0094-y
  24. Shi, 2014, Application of k-means clustering to environmental risk zoning of the chemical industrial area, Front. Environ. Sci. Eng. Front. Environ. Sci. Eng. (Germany), 8, 117, 10.1007/s11783-013-0581-5 · doi:10.1007/s11783-013-0581-5
  25. Siddiqui, 2012, Aggregate nonparametric safety analysis of traffic zones, Accid. Anal. Prev., 45, 317, 10.1016/j.aap.2011.07.019 · doi:10.1016/j.aap.2011.07.019
  26. Suárez Sánchez, 2011, Prediction of work-related accidents according to working conditions using support vector machines, Appl. Math. Comput., 218, 3539
  27. Sugumaran, 2009, Safety analysis on a vibrating prismatic body: A data-mining approach, Expert Syst. Appl., 36, 6605, 10.1016/j.eswa.2008.08.041 · doi:10.1016/j.eswa.2008.08.041
  28. Tao, 2016, A traffic accident morphology diagnostic model based on a rough set decision tree, Transp. Plan. Technol., 39, 751, 10.1080/03081060.2016.1231894 · doi:10.1080/03081060.2016.1231894
  29. Tavakoli Kashani, 2014, A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers, J. Safety Res., 51, 93, 10.1016/j.jsr.2014.09.004 · doi:10.1016/j.jsr.2014.09.004
  30. Thakali, 2016, Model-based versus data-driven approach for road safety analysis: Do more data help?, Transp. Res. Rec., 10.3141/2601-05 · doi:10.3141/2601-05
  31. Upton, 2017, Rapid gravity filtration operational performance assessment and diagnosis for preventative maintenance from on-line data, Chem. Eng. J., 313, 250, 10.1016/j.cej.2016.12.047 · doi:10.1016/j.cej.2016.12.047
  32. van Gulijk, 2018, The case for IT transformation and big data for safety risk management on the GB railways, Proc. Inst. Mech. Eng. Part O J. Risk Reliab., 232, 151
  33. Wang, 2018, Machine vision to alert roadside personnel of night traffic threats, IEEE Trans. Intell. Transp. Syst., 19, 3245, 10.1109/TITS.2017.2772225 · doi:10.1109/tits.2017.2772225
  34. Wang, 2009, Spatial forecast of landslides in Three Gorges based on spatial data mining, Sensors, 9, 2035, 10.3390/s90302035 · doi:10.3390/s90302035
  35. Weng, 2015, In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas, Accid. Anal. Prev., 77, 51, 10.1016/j.aap.2015.02.002 · doi:10.1016/j.aap.2015.02.002
  36. Xiang, 2017, On intelligent risk analysis and critical decision of underwater robotic vehicle, Ocean Eng., 140, 453, 10.1016/j.oceaneng.2017.06.020 · doi:10.1016/j.oceaneng.2017.06.020
  37. Xu, 2011, Real-time transient stability assessment model using extreme learning machine, IET Gener. Transm. Distrib., 5, 314, 10.1049/iet-gtd.2010.0355 · doi:10.1049/iet-gtd.2010.0355
  38. Yang, 2017, Machine learning-based methods for analyzing grade crossing safety. Cluster Comput, Cluster Comput. (Germany), 20, 1625, 10.1007/s10586-016-0714-2 · doi:10.1007/s10586-016-0714-2
  39. Zhang, 1997, Instance-Based Learning for Highway Accident Frequency Prediction, Comput. Civ. Infrastruct. Eng., 12, 287, 10.1111/0885-9507.00064 · doi:10.1111/0885-9507.00064
  40. Zhao, 2018, Cluster analysis of risk factors from near-miss and accident reports in tunneling excavation, J. Constr. Eng. Manag., J. Constr. Eng. Manag. (USA), 144, 14 pp

Cited by

See all 363

Metadata

Authors
Hegde, J., & Rokseth, B.
Venue
Safety Science
Publisher
Elsevier BV
Published
2020-02
Volume
122
Pages
104492
ISSN
0925-7535 (ISSN-L)
DOI
10.1016/j.ssci.2019.09.015
OpenAlex
W2981915020
Cited by
363
References
97
FWCI
37.38
Type
journal article
Language
en
Open access
hybrid
Licence
Funders
Norges Forskningsråd

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Applications of machine learning methods for engineering risk assessment – A review. https://app.cochat.ai/papers/10.1016/j.ssci.2019.09.015
The paper
Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment – A review. Safety Science. https://doi.org/10.1016/j.ssci.2019.09.015

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.