Machine Learning Model for Structural Asset Condition Prediction
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Solution Overview
Problem
Monitoring and preventing adverse conditions at structural assets, such as vegetative overgrowth on utility infrastructure, is costly and inefficient due to the need for frequent manual inspections, which often result in poor performance and significant societal and monetary costs from unplanned outages.
Innovation Solution
A computing system using machine-learning models, specifically neural networks, to predict adverse conditions by analyzing a combination of high-resolution ground-level and lower-resolution overhead imagery, enabling the detection of current and future instances of vegetative overgrowth and other adverse conditions, thereby reducing the need for frequent manual surveys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to monitor structural assets, then detection capability is achieved, but operational cost increases significantly
Solution Approach 1:
The patent replaces manual mechanical inspection systems with automated machine learning-based image analysis. The system uses trained neural networks to automatically detect adverse conditions from images, eliminating the need for human inspectors to physically visit each asset while maintaining or improving detection accuracy.
Solution Approach 2:
The patent creates visual copies (images) of structural assets that can be analyzed remotely. By capturing images of assets and analyzing them through machine learning models, the system allows multiple assessments without requiring physical presence at the asset location, thereby reducing travel and operational costs.
2Measurement precision
If frequent manual inspections are performed to improve detection accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent replaces slow manual inspection processes with automated machine learning analysis that can process multiple images simultaneously. The system achieves high detection accuracy by using trained neural networks that can evaluate assets in parallel, dramatically increasing inspection throughput compared to sequential manual assessments.
Solution Approach 2:
The patent applies machine learning models to analyze more data than traditionally necessary (processing multiple images per asset, including both overhead and ground-level views). This excessive analysis capability ensures high detection accuracy while the automation maintains productivity by efficiently handling the increased data volume.
3Reliability
If comprehensive monitoring of multiple structural assets is implemented, then reliability improves, but device complexity increases
Solution Approach 1:
The patent develops a universal machine learning system that can monitor multiple types of structural assets (utility poles, transmission towers, cell towers, etc.) using the same core technology platform. The trained models are applicable across different asset classes, reducing the need for separate specialized systems for each asset type and thereby managing complexity while expanding monitoring coverage.
Solution Approach 2:
The patent segments the monitoring system into modular components: image acquisition modules, preprocessing modules, machine learning inference modules, and result interpretation modules. This segmentation allows the system to scale to multiple assets by adding more image sources without proportionally increasing overall system complexity, as each component can be independently managed and reused.
Data Source
AI summary
The present disclosure provides systems and methods that use machine-learned models, such as deep neural networks, to predict and prevent adverse conditions at structural assets. One example method includes obtaining data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first structural asset. The plurality of images include at least a first image captured at a first time and a second image captured at a second time that is different than the first time. The method includes inputting data descriptive of at least the first image, the first time, the second image, and the second time into a condition prediction model. The method includes receiving, as an output of the condition prediction model, at least one prediction regarding the occurrence of an adverse condition at the first structural asset during one or more future time periods.