Attribute-Based Safety Risk Assessment. II: Predicting Safety Outcomes Using Generalized Linear Models

Behzad Esmaeili, Matthew R. Hallowell, Balaji Rajagopalan

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


One of the recent advancements in preconstruction safety management is the identification and quantification of risks associated with fundamental attributes of construction work environments that cause injuries. The goal of this paper is to test the validity of using these fundamental risk attributes to predict safety outcomes. The modeling approach required two steps, as follows: (1) a principal component analysis was performed on the safety attributes to reduce dimension of the data and remove collinearity among attributes (the principle component analysis provided insights into the relative importance of the various attributes and provided an orthogonal decomposition of the data), and (2) the leading principal components (which are orthogonal by definition) were used as potential predictors in a generalized linear model with a logit link function to model the probability of different accident categories. The predictive power was then assessed using a rank probability skill score, which quantified the probabilistic skill of the forecasts over the categories. The analysis shows strong predictive skill, making the models attractive for safety managers to use to skilfully forecast the probability of a safety incident given identifiable characteristics of planned work. Researchers in the technology domain may find these models useful in predicting safety outcomes during design, work packaging, and scheduling.

Original languageEnglish (US)
Article number04015022
JournalJournal of Construction Engineering and Management
Issue number8
StatePublished - Aug 1 2015
Externally publishedYes


  • Generalized linear models (GLMs)
  • Labor and personnel issues
  • Predictive models
  • Principal component analysis (PCA)
  • Safety risk management

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management


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