Key predictors of injury severity in occupational accidents involving construction-site vehicles
[EN] Across national statistics, construction repeatedly ranks among sectors with the highest injury and fatality rates. Vehicle-related accidents constitute a modest share of minor injuries yet contribute a signi¿cant fraction of construction fatalities. This study analysed 16,781 Spanish construct...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::97d33992e9b121f0e0fa8d8d9233a26a |
| Acceso en línea: | https://riunet.upv.es/handle/10251/233378 |
| Access Level: | acceso abierto |
| Palabra clave: | Occupational accidents Material agent Construction Vehicles Accident statistics 08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos |
| Sumario: | [EN] Across national statistics, construction repeatedly ranks among sectors with the highest injury and fatality rates. Vehicle-related accidents constitute a modest share of minor injuries yet contribute a signi¿cant fraction of construction fatalities. This study analysed 16,781 Spanish construction vehicle¿related accidents recorded from 2009 to 2022 (2.5% severe-fatal) to identify determinants of injury severity and develop predictive models. Records were retrieved from Delt@, the compulsory national electronic occupational injury reporting platform. Variables were structured into two domains (organisational, contextual) and ¿ve categories. Methods combined descriptive pro¿ling, ¿² association tests, mutual-information ranking and three machine-learning classi¿ers (Random Forest, XGBoost, multilayer perceptron). Seven predictors¿hour block, worker age, job tenure, site zone, deviation pattern, injury type and body region¿showed the strongest association with severity. Separate models were trained on contextual and organisational feature sets. The contextual model detected 87.1% of severe/fatal cases (balanced accuracy 88.1.%), while the organisational model detected 59.3% (balanced ac-curacy 62.1%). The ¿ndings emphasise the importance of scheduling (time-of-day exposure), targeted training for short-tenure and at-risk age groups (30¿59 years old), and control of the site zone. These results provide practical guidance for managers, regulators, engineers and safety practitioners seeking to reduce the number of vehicle-related accidents on construction sites, particularly those with a high level of severity. |
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