Road Surface Damage Detection for Scalable Maintenance Assessment
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Solution Overview
Problem
The sheer scale and geographic extent of infrastructure assets pose challenges in monitoring and maintenance, with current methods being time-consuming and subjective, leading to inefficient deployment of resources and ad-hoc decision-making in maintenance planning.
Innovation Solution
A machine learning model analyzes sensor data from infrastructure assets, using deep learning architectures to extract features and classify damage, and identifies maintenance candidates, providing actionable insights for efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to assess infrastructure assets, then human judgment and flexibility are maintained, but the process becomes time-consuming and subjective
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-based system that uses machine learning models to analyze sensor data and images of infrastructure assets. This substitution eliminates human subjectivity and time constraints while maintaining or improving assessment accuracy through consistent, data-driven evaluation.
Solution Approach 2:
The system creates a digital replica or model of the infrastructure asset condition by processing sensor data and images through machine learning algorithms. This digital copy allows for rapid, repeatable assessment without requiring physical re-inspection, thereby reducing time loss while preserving assessment quality.
2Area of stationary object
If comprehensive monitoring of extensive infrastructure assets is implemented, then complete asset coverage is achieved, but resource deployment becomes inefficient
Solution Approach 1:
The patent divides the extensive infrastructure asset base into manageable segments or individual assets, each assessed independently through automated analysis. This segmentation allows comprehensive coverage of large areas while enabling efficient resource allocation by identifying and prioritizing specific assets that require maintenance attention.
Solution Approach 2:
The system transforms physical infrastructure assets into digital data representations through sensor collection and image capture. By changing the state from physical to digital, the system can analyze and monitor extensive infrastructure coverage efficiently, enabling rapid identification of maintenance needs without proportionally increasing resource deployment requirements.
3Productivity
If automated machine learning models are used to analyze sensor data, then assessment speed and consistency are improved, but system complexity increases
Solution Approach 1:
The patent develops a universal machine learning assessment system that can evaluate multiple types of infrastructure assets (roads, bridges, buildings, etc.) using the same core technology platform. This multi-functionality reduces overall system complexity by avoiding the need for separate specialized systems for each asset type, while maintaining high assessment speed and consistency across diverse applications.
Data Source
AI summary
Systems and methods for assessing infrastructure assets. In some embodiments, sensor data collected from a plurality of infrastructure assets may be analyzed, and an infrastructure asset may be identified as a candidate for a selected type of maintenance. The infrastructure asset may include a road segment, and the sensor data may include an image of a pavement surface along the road segment. One or more machine learning models may be used to identify a portion of the image exhibiting a selected type of damage, such as cracking, chipping, pothole, etc.