Predicting Serious Injury and Fatality Exposure in Construction Industry
Bibliographic record
Abstract
Even though the construction industry has been investing heavily in safety management activities, the fatality rates plateaued over the past years. To take proactive action and prevent such severe incidents in work environments, the ability to make robust predictions related to serious injury and fatality (SIF) exposure is key. Only through such reliable predictions, decision-makers can design on-point interventions, allocate safety resources, and make safety process improvements that could save lives. However, making safety predictions has been a constant challenge for safety researchers due (1) the multi-faceted and dynamic nature of safety systems, and (2) data availability issues caused by dependence on rare and highly contextual incident data. This dissertation therefore aims to (1) to create comprehensive and prioritized list of predictors that includes attributes related to the businesses, projects, and crews to fully capture the construction environments; (2) to evaluate the strengths and weaknesses of existing safety performance measurement metrics to choose a dependent variable that could be used in building robust predictive models; (3) to propose High Energy and Controls Assessment (HECA) as a SIF-focused metric that has statistical predictive power and sufficient data generation capacity; and (4) to build a predictive model to forecast SIF exposure through the analysis of an empirical dataset. To establish the predictive model for SIF exposure, 693 field crew observations were made from 28 businesses and 74 projects in the United States and Canada. This dataset is the first of its kind that includes both safety success and exposure to SIF. Along with these observations, information about the business, project, and crew were collected as potential predictors of SIF exposure. Analysis of this empirical dataset allowed the development of a multi-layer perceptron model that could effectively differentiate safety success from an exposure case using non-linear decision boundaries. Future researchers could use this dissertation in designing improved predictive models, choosing robust variables, and creating new research questions for safety interventions. Future research should seek to address safety data collection challenges through automation that could reduce bias, increase the quality and volume of the data that will avail the generation of better predictive models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".