Georeferenced Data Structures for Civil Infrastructure Asset Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for assessing the condition of civil infrastructure struggle with analyzing and integrating large amounts of disparate visual and non-visual data, as they often rely on two-dimensional images that lack contextual information and fail to effectively organize non-visual rich data, leading to inefficiencies in data storage, analysis, and visualization.
Innovation Solution
The implementation of georeferenced data structures that organize three-dimensional modeling data and non-spatial data with respect to geometry and time, enabling efficient data fusion, visualization, and analysis by discretizing assets into sub-regions and populating these structures with input data, allowing for semantic labeling and intuitive representation of infrastructure conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional images are used for visual inspection, then image resolution can be high, but contextual information is lost and data integration becomes difficult
Solution Approach 1:
The patent transitions from two-dimensional images to three-dimensional point cloud models, adding spatial depth information and contextual relationships. This dimensional change allows the system to maintain high resolution while preserving contextual information through spatial coordinates and geometric relationships, enabling better data integration and analysis.
2Quantity of substance
If large amounts of visual and non-visual data are collected, then data completeness improves, but data organization and analysis become infeasible
Solution Approach 1:
The patent segments the infrastructure asset into discrete sub-regions or zones, with each region having its own data structure. This segmentation allows large amounts of data to be organized hierarchically, making storage and analysis feasible by breaking down the complex dataset into manageable, spatially-organized units that can be processed independently.
3Loss of information
If non-visual data is collected alongside visual data, then information richness increases, but data integration and visualization become challenging
Solution Approach 1:
The patent merges visual data (point cloud models) with non-visual data (sensor readings, inspection records, maintenance history) into a unified georeferenced data structure. This combination integrates heterogeneous data sources by linking them through spatial coordinates, allowing comprehensive information richness while maintaining manageable integration complexity through a common spatial framework.
Data Source
AI summary
Methods and systems for three-dimensional asset modeling. A method includes: initializing a georeferenced data structure for each of a plurality of discrete sub-regions of an asset based on a model representing the asset, wherein the model includes a plurality of points representing features of the asset, wherein each georeferenced data structure includes a subset of the plurality of points representing features of a respective sub-region of the plurality of sub-regions; and populating each georeferenced data structure with input data including three-dimensional (3D) modeling data and nonspatial data, wherein each portion of the input data for a georeferenced data structure is used to populate a respective portion of the georeferenced data structure, wherein the nonspatial data used to populate each georeferenced data structure is organized with respect to geometry of the georeferenced data structure and with respect to time of recording of the nonspatial data.