Computer models provide a proactive approach to jobsite safety that can translate into reduced injuries and more profits.
To win business, several construction business owners have turned to predictive analytics to drive their safety programs. This is especially true in the current economic environment that has led to fewer bidding opportunities with increased bidders and decreased profit levels on those opportunities—sometimes below a competitor’s cost.
How are Predictive Safety Analytics Defined?
Ph.D. researchers at Carnegie Mellon University have developed safety prediction models that return high accuracy rates.
Predictive safety analytics can take several forms. But at its most basic level, it is nothing more than collecting safety data—generally, safety inspection or audit observations on work-site conditions—and then modeling that data to predict when and where safety incidents will occur.
Once incidents can be predicted, corrective actions can be taken to prevent them. By taking a proactive and preventive approach to safety, companies can clearly differentiate themselves from their competitors and communicate added value to prospective customers.
How Do Predictive Safety Analytics Work?
Predictive safety models are built by giving computers safety inspection or audit observations. The computers then find the patterns in the data and construct a predictive model. Researchers test the accuracy of these models by giving them a different set of observation data and asking the models to predict the number of incidents. These incident predictions are then checked against the actual incidents, which are reserved and not exposed to the models (for accuracy).
Once these models are fully tested and used, contractors can simply feed the model new and current observation data, and it will predict, with accuracy rates of 80 percent or better, what future incident levels will be.
