Data Analysis and Predictive Analytics to Improve Construction Safety

May 10, 2024

Despite a dedicated effort towards improved safety regulations, the number of construction-related accidents and fatalities has risen over recent years. Construction employees risk their lives every day for the protection of our critical infrastructure, architecture, water supply, electricity, and more. However, many employees fall victim to preventable accidents – risks that could have been avoided through the use of data and technology. To improve employee safety, the construction industry is shifting its perspective towards technology, with a new focus on the massive amounts of data that can be collected on a worksite. Through data analysis and predictive analytics, construction management can better identify accident trends and patterns with improved accuracy for employee protection. 

Leveraging Data Analysis 

Data from incident reports, employee observations, worker wearables, and even weather information offers valuable insight into safety trends and patterns. By analyzing this data, construction management can uncover correlations such as which tasks pose higher risks, whether adverse weather conditions coincide with safety incidents, or if fatigue contributes to accidents at specific times of the day. 

Data analytics goes beyond identifying trends, though – it provides real-time insights that can empower strategic decision-making and enhances efficiency while reducing risks. Data can identify the root causes of accidents, allowing for targeted solutions for prevention. This change offers a powerful shift – moving away from anecdotal evidence towards measurable data that empowers safety departments, fosters continuous improvement, and ultimately, creates safer workplaces. 

Predicting the Future through Predictive Analytics 

Predictive analytics empowers a proactive safety approach. Instead of simply reacting to accidents, companies can identify and address potential risks before they happen. Using historical data and statistical models, companies can pinpoint specific safety concerns and focus interventions where they'll have the biggest impact. Data-driven decisions foster a safety culture built on evidence rather than assumptions or guesses. 

Data for predictive analytics is fed through complex models that consider various factors and their historical impact on safety. The model learns from patterns and relationships within the data and once trained, can predict the likelihood of safety incidents happening in certain situations. This can be based on specific tasks like working at certain heights, weather conditions, worker fatigue, or combinations of these and similar factors. 

On a construction site, for example, wearable technology can track worker vitals, location, and even posture. This data can be fed into predictive models to identify potential risks and unsafe work practices in real-time, allowing for immediate intervention. Predictive models might even analyze a worker's experience level or time on site to forecast their potential accident risk. This allows for better team building and resource allocation. 

The Future of Safety 

By determining high-risk scenarios, companies can take preventive measures. This might involve increased supervision, additional safety training for specific tasks, or even temporarily halting work until conditions improve. Safety is prioritized by focusing resources on areas with the highest risk of incidents. Preventing accidents not only protects workers but also saves companies money. Fewer accidents translate to lower costs associated with lawsuits, insurance claims, and lost productivity due to injuries. In fact, accidents and fatalities on the job cost the construction industry $13 billion in lost production, fines, and settlements in 2022 alone. 

Maximizing Technology's Potential

Despite the impressive power of data and predictive analysis, the accuracy of these predictions relies heavily on the quality and completeness of the data used to train the models. If the data used is biased or incomplete, the model's predictions may prove unreliable. Because of this, predictive analytics can support, but not replace, common sense and established safety protocols. 

The combined power of data analytics and predictive analytics has the potential to significantly decrease the number of accidents and injuries on construction sites. This translates to a safer work environment for everyone, lower costs for companies, and improved worker morale. By embracing these data-driven approaches, the construction industry can move towards a future where safety is no longer an afterthought, but a priority built on proactive risk management. 

A Career in Construction Management 

Capitol Technology University’s programs in Construction and Facilities put you at the forefront of industrial knowledge and skills that will help you succeed in this dynamic field. Within our programs, students learn how technology and construction intersect for the development of safer methods and improved outcomes. For more information on how to build your construction career, contact our Admissions team