Construction Safety Risk Visualization Using AI Risk Scoring
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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, leading to underreported incidents and inadequate safety management.
Innovation Solution
A system and method for determining and visualizing safety risk scores through the use of machine learning and artificial intelligence, combining multiple sources of safety information to assign numerical safety risk values to entities involved in construction projects, and providing interactive visualizations to facilitate proactive safety improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional safety reporting methods are used, then incident reporting is simple and quick, but safety risks are not identified and presented in a timely and cohesive manner
Solution Approach 1:
The patent combines multiple data sources including incident reports, inspection data, weather information, and project metadata into a unified safety risk analysis system. This integration allows comprehensive safety risk identification while maintaining timely presentation through automated processing and visualization dashboards.
Solution Approach 2:
The system replaces manual safety risk analysis with automated machine learning models and algorithms. These computational systems process safety data, identify patterns, and generate risk assessments automatically, eliminating the time-consuming manual analysis while improving accuracy through consistent application of safety criteria.
2Reliability
If comprehensive safety data collection is implemented, then safety risk analysis becomes more accurate, but system complexity increases
Solution Approach 1:
The patent segments the safety risk analysis system into distinct functional modules: data collection components, data processing components, machine learning model components, and visualization components. This modular architecture allows comprehensive safety data collection while managing complexity through organized, reusable modules that can be independently developed and maintained.
Solution Approach 2:
The system introduces standardized data interfaces and processing layers that act as intermediaries between diverse data sources and the analysis engine. These intermediaries normalize different data formats and structures, enabling comprehensive data collection from multiple sources without increasing overall system complexity.
3Productivity
If manual safety incident reporting is used, then implementation is straightforward, but safety incidents are underreported and safety management is inadequate
Solution Approach 1:
The system implements automated feedback mechanisms where safety risk assessments are continuously updated based on new incident reports, inspection data, and pattern recognition. This feedback loop encourages comprehensive reporting by showing immediate value and impact, reducing underreporting while maintaining straightforward implementation through automated processing.
Solution Approach 2:
The system enables automated safety risk identification and presentation without requiring manual intervention for data processing. The machine learning models automatically analyze reported incidents and generate safety insights, allowing the system to serve itself in processing and analyzing safety data, thereby improving both efficiency and information completeness.
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.