Construction Safety Risk Visualization Using AI and IoT Data
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Solution Overview
Problem
The construction industry faces significant safety risks, with one out of five workplace deaths in the U.S. attributed to construction-related fatalities, and existing methods fail to effectively identify and present safety risks in a timely and cohesive manner, impacting contractor insurance rates and profits.
Innovation Solution
A system and method for determining and visualizing safety risk scores using machine learning and artificial intelligence to assign numerical safety risk values to entities involved in construction projects, incorporating multiple sources of safety information and providing graphical user interfaces for improved safety management, leveraging mobile devices, IoT, drones, cameras, and sensors to capture and analyze data for real-time risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional safety monitoring methods are used in construction, then implementation simplicity is maintained, but safety risk detection precision and timeliness deteriorate
Solution Approach 1:
The system segments safety monitoring into multiple specialized components: computer vision modules for hazard detection, IoT sensors for environmental monitoring, wearable devices for worker safety tracking, and AI analysis engines. Each component handles specific aspects of safety monitoring independently, then integrates results to provide comprehensive risk assessment without overwhelming system complexity
Solution Approach 2:
The patent introduces a centralized safety risk analysis system that acts as an intermediary between various data sources (cameras, sensors, wearables) and stakeholders (workers, managers, regulators). This intermediary aggregates, analyzes, and presents safety information through user interfaces, reducing the complexity burden on individual components while maintaining high detection precision
2Reliability
If comprehensive safety data collection is implemented, then safety risk analysis accuracy improves, but information processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing safety data at the source: computer vision models pre-analyze video feeds for hazards, IoT sensors pre-filter environmental data, and wearables pre-process biometric information. This preliminary analysis reduces data volume and identifies critical risks before central aggregation, maintaining accuracy while reducing processing time
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with AI-driven automated analysis. Machine learning models automatically detect safety hazards, predict risks, and generate alerts without manual intervention, significantly reducing processing time while improving accuracy compared to human-based safety monitoring
3Speed
If real-time safety monitoring is implemented, then safety risk response time improves, but energy consumption and system cost increase
Solution Approach 1:
The system implements periodic monitoring at different intervals based on risk levels: high-risk areas receive continuous real-time monitoring, medium-risk areas receive periodic automated scans, and low-risk areas receive intermittent checks. This periodic action maintains safety response effectiveness while reducing overall energy consumption compared to uniform continuous monitoring across all areas
Solution Approach 2:
The patent dynamically adjusts monitoring parameters based on detected conditions: increases monitoring frequency when hazards are detected, reduces frequency during safe periods, and adapts sensor sensitivity and camera frame rates according to environmental conditions and risk levels, optimizing energy consumption while maintaining rapid response capability
Data Source
AI summary
A computer-implemented method and system provide the ability to determine and provide a safety risk analysis for construction. Construction related data is obtained and includes textual data and a visual artifact for the construction project. A construction safety context is identified based on the construction related data. Based on the construction safety context, a safety participant risk score that assigns a numerical safety risk participant value to any entity involved in the construction project is determined. Based on the safety risk participant score, a safety project score that assigns a risk level on a per-project basis is determined. The safety risk analysis is presented based on the safety participant risk score and safety project score, via a graphical user interface.