  By building on a recently introduced genetic-inspired attribute-based
conceptual framework for safety risk analysis, we propose a novel methodology
to compute construction univariate and bivariate construction safety risk at a
situational level. Our fully data-driven approach provides construction
practitioners and academicians with an easy and automated way of extracting
valuable empirical insights from databases of unstructured textual injury
reports. By applying our methodology on an attribute and outcome dataset
directly obtained from 814 injury reports, we show that the frequency-magnitude
distribution of construction safety risk is very similar to that of natural
phenomena such as precipitation or earthquakes. Motivated by this observation,
and drawing on state-of-the-art techniques in hydroclimatology and insurance,
we introduce univariate and bivariate nonparametric stochastic safety risk
generators, based on Kernel Density Estimators and Copulas. These generators
enable the user to produce large numbers of synthetic safety risk values
faithfully to the original data, allowing safetyrelated decision-making under
uncertainty to be grounded on extensive empirical evidence. Just like the
accurate modeling and simulation of natural phenomena such as wind or
streamflow is indispensable to successful structure dimensioning or water
reservoir management, we posit that improving construction safety calls for the
accurate modeling, simulation, and assessment of safety risk. The underlying
assumption is that like natural phenomena, construction safety may benefit from
being studied in an empirical and quantitative way rather than qualitatively
which is the current industry standard. Finally, a side but interesting finding
is that attributes related to high energy levels and to human error emerge as
strong risk shapers on the dataset we used to illustrate our methodology.
