Temporal GNN Construction Status Assessment from BIM and Scan Data
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Solution Overview
Problem
Traditional construction quality assessment methods in the AEC industry are labor-intensive, prone to human error, and struggle with incomplete data due to sensor noise, weather conditions, and dynamic environments, leading to inaccurate assessments of built environments.
Innovation Solution
A system using a graph neural network (GNN) processes build data, scan data, and scheduling data to generate a dynamic spatio-temporal graph, incorporating hierarchical and temporal relationships, enabling accurate construction status determination by aligning and integrating diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual inspection methods are used, then labor intensity is high and time consumption is significant, but the system complexity is low and ease of operation is maintained
Solution Approach 1:
The patent replaces manual inspection mechanisms with an automated system that uses graph neural networks to process building information models, scan data, and scheduling data. The GNN automatically identifies construction status, quality issues, and progress metrics without human intervention, substituting mechanical manual inspection with computational analysis to achieve high-speed automated assessment.
Solution Approach 2:
The system creates a digital copy of the physical built environment through building information models and scan data. This virtual representation allows the system to analyze construction quality and status by processing digital data rather than physically inspecting each component, enabling rapid automated assessment of the entire construction site.
2Measurement precision
If automated systems with multiple data sources are implemented, then measurement precision and assessment accuracy improve, but data incompleteness and noise from sensor errors, weather conditions, and dynamic environments reduce reliability
Solution Approach 1:
The patent merges multiple data sources including building information models, scan data, and scheduling data into a unified graph structure. By combining these diverse data types and processing them through the graph neural network, the system compensates for incompleteness in individual data sources using contextual information from other sources, thereby maintaining high assessment accuracy despite environmental noise and data gaps.
3Measurement precision
If comprehensive analysis of semantic relationships between components is performed, then construction quality assessment accuracy improves, but computational time and processing complexity increase
Solution Approach 1:
The system pre-processes and structures building information models, scan data, and scheduling data into graph representations before quality assessment. This preliminary organization of data into semantic relationships allows the graph neural network to efficiently process and analyze connections between components during actual assessment operations, reducing real-time processing requirements while maintaining comprehensive analysis capability.
Data Source
AI summary
Systems and methods for determining a status of a built environment using a graph neural network are provided. An example system may obtain build data including a building information model (BIM) of the built environment, scan data depicting the built environment, and scheduling data indicating tasks for constructing the built environment. The system may perform a registration of the scan data and build data, and generate BIM-based features indicating hierarchical relationships of elements of the BIM, scan-based features indicating characteristics of scans of the built environment, and scheduling-based features indicating characteristics of the built tasks. The system may provide the build data, the BIM-based features, the scan data, the scan-based features, the scheduling data, and the scheduling-based features to a temporal graph neural network to generate a graph, and the status of the built environment based upon the graph. The system may provide the built status to a computing device.