AEC Risk Analysis System Using ML for Issue Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Construction sites face challenges in managing and prioritizing numerous project issues in real-time, as team leaders struggle to identify critical risks and their impact on construction projects, leading to inefficiencies in resource allocation and project progression.
Innovation Solution
A system combining machine learning models with data from mobile devices, drones, cameras, and sensors to analyze and visualize risk metrics across projects and subcontractors, providing interactive visualizations and feedback loops to enhance risk management and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervisors manually review long lists of construction issues, then they can identify critical risks using human knowledge, but it consumes an entire day and reduces productivity
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated machine learning system that uses natural language processing and classification algorithms to analyze construction issues, thereby maintaining risk identification accuracy while dramatically improving review efficiency
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw construction issue data and supervisor decision-making, automatically prioritizing issues based on learned patterns from historical data while allowing supervisors to focus on critical decisions
2Productivity
If machine learning models automatically prioritize risks, then productivity increases and time is reduced, but the system lacks the contextual understanding of human supervisors
Solution Approach 1:
The patent implements feedback loops where supervisor corrections and validations of automated risk prioritization are fed back into the machine learning model, continuously improving its accuracy and contextual understanding while maintaining high productivity
Solution Approach 2:
The patent merges automated machine learning risk prioritization with human supervisor expertise by allowing interactive adjustment of risk scores and incorporating supervisor feedback, thereby combining the speed of automation with the contextual understanding of human experts
3Measurement precision
If the system analyzes all construction issues comprehensively, then measurement precision improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex risk analysis system into modular components including data ingestion modules, machine learning modeling modules, visualization modules, and feedback processing modules, allowing comprehensive analysis while managing complexity through organized separation of concerns
Data Source
AI summary
A system and method provide the ability to control an architecture, engineering, and construction (AEC) project workflow. AEC data regarding a quality of construction is obtained. A set of classifiers and machine learning models are obtained. The AEC data is augmented based on the set of classifiers and machine learning models. A risk metric is generated for one or more issues in the AEC data based on the augmented AEC data. The risk metric is interactively generated and presented on a display device. Work, project resourcing, and/or training are prioritized based on the risk metric.