Shayboun, M; Kifokeris, D and Koch, C (2020) Machine learning for analysis of occupational accidents registration data. In: Scott, L and Neilson, C J (eds.) Proceedings of 36th Annual ARCOM Conference, 7-8 September 2020, Online Event, UK.
Abstract
Regardless of the efforts by employers and public organizations to eliminate occupational accidents, the latter are a persistent problem in the construction industry. In the Swedish construction context, there is a desire to identify causes and factors playing a role in work-related accident prevention, as there are large underused databases of collected registrations representing knowledge on causes and context of accidents. The aim of the current contribution is to review the application of machine learning (ML) for the improved prevention of accidents and corresponding injuries, and to identify current limitations. A systematic literature review on the use of ML for analysing accident records data was carried out. In the reviewed literature, ML was applied in the prediction of accidents or their outcome and extracting or identifying causes affecting the risks of injuries. The algorithms used were diverse; Artificial Neural Networks, k-nearest neighbour, logistic regression, Naive Bayesian, Decision Tree and Support Vector Machines, Random Forest, AdaBoost analysis, and Stochastic Gradient Tree Boosting. The results point to the identification of accident-related objects and factors such as placement in time, project characteristics, congested and/or confined workplace, poor visibility, lack of preparation, and safety behaviour. ML combined with data mining techniques such as Natural Language Processing and graph mining, appears to be beneficial in discovering unknown associations between different features and in multiple levels of clusters. However, the research on ML in accident prevention is at an early stage. A consensus regarding the algorithms' performance and prediction accuracy benchmarks, has not been achieved. Future research needs to focus on methods addressing the problem of unbalanced data, improving accident recording process, merging different data sources and research into more attributes (such as risk management), applying deep learning algorithms, and improve the testing accuracy of ML models.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | accident registration; machine learning; occupational accident prevention. |
| Index terms: | prevention, database, accuracy, deep learning, risk management, mining, machine learning, occupational accident, placement, logistic regression, forest, data mining, testing, safety behaviour, systematic literature review, accident prevention, construction industry, artificial neural network, decision tree, injury |
| Subjects: | health conditions and diseases, artificial intelligence, management, professional development, data management, risk assessment, modelling and simulation, health risk and incident analysis, financial risk, professional practice, environmental science, decision analysis, statistical analysis, occupational health and safety management, research evaluation and metrics, environmental health, geotechnical engineering, data science, industry analysis |
| Topics: | Risk Management, Information Management, Engineering Principles, Sustainability, Health and Safety, Cost Management, Human Resources, Digital Applications, Research Practice |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here