Construction Hazard Prediction Using ML and NLP Risk Assessment
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Solution Overview
Problem
Traditional methods for identifying safety hazards and assessing risk exposures in construction projects rely on manual analysis and human judgment, which are time-consuming and costly.
Innovation Solution
A system and method using machine-learned models to predict hazards, impact areas, and recommend mitigation actions based on historical safety data, leveraging natural language processing algorithms to enhance the accuracy and efficiency of risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and human judgment are used to identify safety hazards and assess risk exposures, then the process can be performed with simple tools and methods, but it is time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses historical safety data to predict hazards and assess risks. The system automatically processes project information, identifies hazards, and generates risk assessments without requiring manual human analysis, thereby reducing time consumption while maintaining or improving accuracy through data-driven predictions.
Solution Approach 2:
The system enables self-service by automatically generating hazard predictions and risk assessments without requiring expert human intervention. The machine learning models autonomously analyze project data, identify potential hazards, and provide risk evaluations, allowing the system to serve itself in performing tasks that traditionally required human safety experts.
2Productivity
If machine-learned models are used to predict hazards and assess risks, then time consumption and cost are reduced, but the system complexity increases
Solution Approach 1:
The patent segments the risk assessment system into distinct machine learning models, each trained on specific historical safety data for different types of hazards. This modular segmentation allows the system to handle complex predictions through specialized components, improving productivity while managing complexity through organized, independent model units that can be developed and maintained separately.
Solution Approach 2:
The machine learning system is designed as a universal platform that can predict multiple types of hazards across different construction projects using the same underlying infrastructure. The system processes various project inputs and generates comprehensive risk assessments for diverse scenarios, achieving multi-functionality that improves productivity without proportionally increasing system complexity.
3Reliability
If historical safety data is used to train machine learning models, then the accuracy of hazard prediction is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical safety data before actual hazard prediction is needed. The models are trained in advance on comprehensive historical datasets, storing learned patterns and relationships. During actual use, the pre-trained models can quickly predict hazards with high reliability without requiring intensive real-time computational resources, as the heavy processing was performed beforehand during training.
Data Source
AI summary
A method for determining a predicted hazard, an impact area, a mitigation action and a risk assessment score for an activity. The method includes obtaining a future activity and predicting, using a first machine-learned model, a predicted hazard for the future activity. The method further includes predicting, using the predicted hazard and a second machine-learned model, an impact area for with the predicted hazard. The method further includes determining, using the predicted hazard, the historical safety data and a natural language processing algorithm, a mitigation action for the predicted hazard and a risk assessment score for the predicted hazard; and planning the project using the predicted hazard, the impact area, the mitigation action and the risk assessment score.