Construction Safety Risk Modeling and Simulation

by   Antoine J. -P. Tixier, et al.

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.



There are no comments yet.


page 33

page 34


Automatically Learning Construction Injury Precursors from Text

In light of the increasing availability of digitally recorded safety rep...

AI Predicts Independent Construction Safety Outcomes from Universal Attributes

This paper significantly improves on, and finishes to validate, the appr...

Safety Practice and its Practitioners: Exploring a Diverse Profession

System safety refers to a diverse engineering discipline assessing and i...

Simulation-Based Analytics for Fabrication Quality-Associated Decision Support

Automated, data-driven quality management systems, which facilitate the ...

Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving

Balancing safety and efficiency when planning in dense traffic is challe...

New complex network building methodology for High Level Classification based on attribute-attribute interaction

High-level classification algorithms focus on the interactions between i...

Risk Quantization by Magnitude and Propensity

We propose a novel approach in the assessment of a random risk variable ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.