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In this study, we capitalized on a collective dataset repository of 57kaccidents from 9 companies belonging to 3 domains and tested whether modelstrained on multiple datasets (generic models) predicted safety outcomes betterthan the company-specific models. We experimented with full generic models(trained on all data), per-domain generic models (construction, electric T&D,oil & gas), and with ensembles of generic and specific models. Results are verypositive, with generic models outperforming the company-specific models in mostcases while also generating finer-grained, hence more useful, forecasts.Successful generic models remove the needs for training company-specificmodels, saving a lot of time and resources, and give small companies, whoseaccident datasets are too limited to train their own models, access to safetyoutcome predictions. It may still however be advantageous to train specificmodels to get an extra boost in performance through ensembling with the genericmodels. Overall, by learning lessons from a pool of datasets whose accumulatedexperience far exceeds that of any single company, and making these lessonseasily accessible in the form of simple forecasts, generic models tackle theholy grail of safety cross-organizational learning and dissemination in theconstruction industry.

Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models