Infrastructure Asset Prioritization via Multi-Source Inspection Data
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Solution Overview
Problem
Aging infrastructure and increased regulatory demands necessitate a machine-based inspection process for steel and concrete assets that prioritizes rehabilitation based on severity and geospatial visualization, as previous methods lacked calculated analysis and comparative assessment across similar industries or regulatory conditions.
Innovation Solution
A system and method for infrastructure inspection and prioritization using data from remote-operated vehicles, unmanned aerial vehicles, and handheld devices, which generates a prioritization matrix and three-dimensional visualization to determine corrosion, structural safety, and predict failure, employing machine learning for weighted prioritization and geospatial representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-based inspection with data collection and analysis is implemented, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The inspection system is segmented into multiple specialized components: remote-operated vehicles for data collection, unmanned aerial vehicles for aerial inspection, handheld devices for on-site measurement, and separate processing modules for data analysis. Each component performs a specific function, reducing the complexity of any single device while maintaining high overall measurement precision through integrated operation.
Solution Approach 2:
A centralized processing module acts as an intermediary that receives data from multiple inspection devices, performs weighted prioritization analysis, and generates rehabilitation recommendations. This intermediary consolidates the computational complexity, allowing individual inspection devices to remain relatively simple while achieving high measurement precision through coordinated data processing.
2Loss of information
If comprehensive data collection from multiple sources is performed, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining inspection parameters, data collection protocols, and analysis frameworks before field operations begin. Inspection checklists and data structures are prepared in advance, allowing rapid data collection during field operations without compromising information completeness. This reduces on-site time while ensuring comprehensive information gathering.
Solution Approach 2:
Manual data collection and analysis methods are replaced with automated electronic systems including remote-operated vehicles, unmanned aerial vehicles, and digital data processing tools. This substitution eliminates time-consuming manual processes while capturing comprehensive infrastructure condition information through automated sensing and analysis algorithms.
3Reliability
If weighted prioritization and failure prediction are implemented, then rehabilitation effectiveness is improved, but device complexity increases
Solution Approach 1:
The system transforms raw inspection data into prioritization scores by applying weighted parameters and failure prediction models. Different asset types receive customized weightings based on their specific failure modes and regulatory requirements. This parameter-based approach improves rehabilitation effectiveness by focusing resources on highest-risk assets while keeping the analysis system adaptable rather than overly complex.
Solution Approach 2:
The prioritization system applies local quality by tailoring analysis parameters and failure prediction models to specific asset types, locations, and regulatory contexts. Each infrastructure asset is evaluated using locally-relevant criteria rather than a uniform approach, improving prioritization accuracy while allowing the system to remain modular and manageable in complexity.
Data Source
AI summary
A novel process and software to conduct infrastructure inspections and prioritization on steel and concrete assets. This is done by using asset specific inspection process to include: data collected from inspection check list, data collected from unmanned underwater remote operated vehicles (ROV), data collection from unmanned aerial vehicles (Lidar and Photogrammetry), and data collected from handheld devices. The combination of this data is fed into a software-based matrix of prioritizing an asset type by severity of conditions and visualizing that data geospatially. This technology calculates the percentage of corrosion on the assets, uses measurements on structural or safety aspects of the asset, and creates a three-dimensional (virtual) visualization and geospatial representation of these assets.