Key predictors of injury severity in occupational accidents involving construction-site vehicles

• Large dataset on occupational accidents involving construction vehicles was studied • Chi-squared statistic (χ²) and mutual information identify seven severity drivers • Worker age, body part injured, injury & deviation as the strongest severity factors • Two Neural Networks model development (CVAC_O & CVAC-C) detects 69 % critical cases • Practical measures set for small contractors, low-experienced & middle-aged workers 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 significant 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 five categories. Methods combined descriptive profiling, χ² association tests, mutual-information ranking and three machine-learning classifiers (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 accuracy 62.1%). The findings 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.