The Role of Predictive Analysis in Boosting Construction Safety

Construction sites can be dangerous places to work, with skilled professionals required to navigate multiple potential hazards associated with heavy machinery, elevated workspaces, powerful equipment and adverse weather conditions. In 2022, almost one in five workplace deaths occurred in the construction industry, with nearly 40% caused by slips, trips and falls. 

To protect workers from the many potential dangers associated with the industry, robust and regularly reviewed safety policies are essential. Aided by professional guidance provided by regulatory bodies like OSHA, employers must adapt safety policies to address unique needs.

In the past, such adjustments were mostly devised through manual investigations into health and safety records, taking up a lot of time and resources. Today, aided by data analytics and machine learning algorithms, high-quality insights into actionable safety adjustments can be generated much quicker, this is the role of predictive analysis in boosting construction safety.

What Is Predictive Analytics?

Predictive analytics concerns the leveraging of data to accurately predict future trends and events. Specialized computer systems review historical data to identify patterns in specific practices, with modern AI and machine learning technologies capable of detecting minutiae that often goes unnoticed by humans, allowing for highly-accurate and insightful predictions.

How Is Predictive Analysis Used in the Construction Industry?

Predictive analysis can help to improve many elements of the construction industry, ranging from resource allocation and site management enhancements to the optimization of wider organizational decision-making processes. Provided a business has the ability to collect data pertaining to a certain process, predictive analysis can be used to accurately forecast trends.

In terms of construction safety, predictive analysis helps employers identify areas of concern that should be prioritized in safety policies. Through the analysis of past accidents, injuries and near-misses, trends can be accurately identified, enabling business leaders and safety teams to divert resources to protective and preventive measures most likely to be effective.

Leveraging Analytics to Identify Construction Safety Risks

Predictive analytics can only be leveraged if high volumes of relevant data are collected, so it’s essential that safety teams gather information from multiple sources. In the construction industry, high-quality safety data can be collected from numerous sources. Below are just a few examples of safety data collection points commonly used in the construction industry.

• Inspection data: Site inspections performed internally or externally can be used to highlight potential safety hazards and assess general safety conditions. The details of these reports over time can be algorithmically analyzed to uncover hazard trends.
• Accident reports: Records of all accidents, injuries and near-misses reported on a specific construction site can be used to identify areas and practices that commonly produce safety risks, with predictive analytics helping to prioritize areas of concern.
• Safety audits: Jobsite safety audits help leaders assess the efficacy of construction safety policies themselves, providing teams high-quality insights into the benefits and shortcomings of new policies to help them identify gaps that may need addressing.
• Security devices: Commercial security cameras with integrated AI analytics tools can be programmed to autonomously detect and flag footage of hazards and safety issues on construction sites, providing teams high-quality data that can be used to assess jobsite safety.
• Wearable technologies: Smart sensors and similar data collection devices installed in wearable equipment like helmets and vests continuously analyze movements and health signifiers to produce legible data pertaining to the physical health of workers.
• Machine monitors: Internet of Things (IoT) sensors attuned to monitor key aspects of a machine’s operation and usage produce data that can be used to improve safety practices, for example, by ensuring machines receive required regular maintenance.
• Environmental sensors: Air quality, sound, humidity and heat sensors installed in key areas around a work site can help safety leaders identify potentially hazardous conditions, with predictive analysis helping to address issues before impacts are felt.

How Predictive Analysis Boosts Construction Safety

Using the above information as a foundation, predictive analytics programs can help leaders identify potential construction safety issues before hazards impact workers. For example, the analysis of accident reports may highlight that a majority of injuries occur within one location or practice, enabling safety leaders to focus resources on strengthening policies in that area.

This same principle is applied across all data sources, producing high-quality, legible reports detailing areas of concern and suggested improvements. Multiple data sources can combine to produce even more detailed insights. For instance, wearable device data may help explain why injuries occur so often in select areas, revealing workers to be too tired, stressed or hot.

Theories can be reaffirmed by supporting evidence collected by wider data sources, such as environmental sensors and scheduling data, helping to secure buy-in from decision-makers that safety protocols need to be adjusted. With a good understanding of how data suggests the occurrence of key hazards, algorithms can be run to predict risk levels across a worksite.

The Future of Predictive Analytics in Construction 

While the benefits of leveraging predictive analytics to improve construction safety may be clear to some professionals, the widespread adoption of such processes isn’t quite a reality just yet. Recently-published data suggests less than 4% of US construction companies use AI at present, though around 90% are planning to use data analytics tools in the near future.

As predictive analytics processes become more advanced, and the implementation of such solutions becomes more intuitive, the potential for more businesses to adopt these practices may become greater. One recent report on the topic found 44% of construction leaders think the industry is ready to invest more heavily in predictive analytics, with 68% believing clients would pay more for its use, suggesting a bright future for predictive analytics in construction.