From physical hazards to training inequities and demanding schedules, construction is one of the most dangerous industries in the United States. The construction trades rank fourth in the U.S. Bureau of Labor Statistics' most recent report identifying the 10 civilian occupations with the highest fatal work injury rates.
Common physical hazards on construction sites include falls/slips/trips, objects dropping from heights, and physical harm from proximity to equipment. According to an article in Safety+Health magazine, nearly four out of five construction workers cite the need for increased training to deal with safety hazards. Delivering effective safety training is a continuous challenge that demands ongoing vigilance from every individual, particularly as employees face the daily pressure to meet productivity targets. Additionally, with tasks frequently changing, there are always new opportunities for tools and equipment to be misplaced, introducing additional safety risks.
Given the technological advancements we've achieved, how can we reduce the number of accidents in construction zones? From an AI standpoint, expanding the use of smart vision and machine learning solutions in construction can enhance worker safety, shifting it from reactive to proactive.
Predictive analytics can be implemented to alert workers of potential risk. By collecting physical, visual, and environmental data from incident reports and worker wearables (e.g., smart helmets, connected wristbands, body cameras, etc.), AI can learn to identify equipment malfunctions, prioritize the likelihood and impact of potential risks, and understand how each decision impacts future actions. In addition, predictive analytics can optimize equipment maintenance efforts by predicting a successive series of imminent failures.
Machine learning and computer vision models can be embedded into site infrastructure. Models can be trained to perform specific functions to streamline efficiencies while improving safety in a work zone or construction site, including:
- Control access. Site security can be managed with surveillance systems equipped with AI-powered cameras. Facial recognition and behavioral analysis are two ways these models can perform identity checks and flag suspicious activity.
- Personal Protective Equipment (PPE) detection. Computer vision models can be trained to automatically identify different types of safety gear, including boots, headgear, and safety vests. By identifying PPE usage, construction/safety managers can improve worker training on best practices and better prepare for safety audits.
- Inspections. Traditional monitoring methods such as onsite visual inspections can be enhanced with remote computer vision monitoring or augmented/virtual reality captured by drones. This opens up new opportunities to access remote locations that may be more difficult or dangerous for inspectors to get to.
- Hazard detection. AI is capable of detecting and alerting construction managers of job site hazards, including backover accidents, slips, trips, or falls, improper ladder or other tool use, electrical hazards via torn/frayed wires, overloaded circuits, mishaps in confined workspaces, and improper trenching and shoring.
- Ergonomics monitoring. AI models can be trained to analyze video streaming from a construction site to track a worker's repetitive motions, such as bending, and determine the level of ergonomic stress. From this analysis, training can be implemented to reduce injury.