Road Inspection Data Fusion for Real-Time Maintenance Decisions
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Solution Overview
Problem
Existing road design and maintenance practices rely on theoretical data and visual observations, failing to account for actual road conditions, environmental impacts, and regional best practices, leading to insufficient road quality assessments and maintenance.
Innovation Solution
A system and method that integrates real-time data collection and analysis from multiple sources, including design, construction, maintenance, and environmental data, using AI to derive best practices and alter execution parameters in real-time to improve road quality indexes, reduce environmental impact, and enhance sustainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road design and maintenance rely on theoretical data and visual observations, then implementation is simple and quick, but road quality assessment is insufficient and inaccurate
Solution Approach 1:
The patent merges multiple data sources including sensor data from vehicles, environmental data, construction data, and maintenance data into a unified road quality assessment system. This integration allows comprehensive evaluation of road conditions by combining information from various sources rather than relying on single-method assessments.
Solution Approach 2:
The system is designed to handle multiple types of data collection and analysis functions within a single platform. It can assess road conditions, evaluate environmental impacts, track maintenance effectiveness, and provide predictive analytics, making it a multi-functional solution that addresses various road management needs simultaneously.
2Reliability
If comprehensive data collection and AI analysis are implemented, then road quality and sustainability improve, but system complexity and implementation cost increase
Solution Approach 1:
The system incorporates automated data collection through sensors embedded in vehicles and infrastructure, which continuously monitor road conditions without requiring manual intervention. The AI algorithms automatically analyze the collected data and generate maintenance recommendations, reducing the need for expert manual analysis and simplifying operations.
Solution Approach 2:
The system establishes continuous feedback loops where sensor data from the field is immediately analyzed by AI algorithms, and results are fed back to decision-makers in real-time. This allows for dynamic adjustment of maintenance strategies based on actual road conditions, improving reliability while maintaining manageable complexity through automated closed-loop control.
3Duration of action of stationary object
If real-time data-driven decision-making is used, then road quality and longevity improve, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and filtering at the data collection stage, preparing data for analysis before it reaches the AI algorithms. By pre-processing data to remove noise and organize information, the system reduces the computational burden on subsequent analysis stages, lowering energy requirements while maintaining accurate road quality assessment for longevity improvement.
4Adaptability or versatility
If traditional cracking rate and IRI metrics are used, then assessment is simple and quick, but environmental impacts and regional best practices are not considered
Solution Approach 1:
The patent adds new dimensions to traditional road assessment by incorporating environmental data, construction history, and maintenance records alongside conventional metrics like cracking rate and IRI. This multi-dimensional approach allows the system to evaluate road quality while considering environmental impacts and regional contexts, providing a more comprehensive and adaptable assessment framework.
Data Source
AI summary
A method, system and computer readable medium for improving road quality indexes comprising, for each road R1 from a plurality of roads, obtaining two of more datasets comprising: design data, construction data, maintenance data, inspection data, environmental data; from one or more moments in the lifecycle of the road R1. Deriving best practices related to road construction and/or road maintenance is performed considering the obtained datasets. During an intervention performed on a road R2, altering in real-time execution parameters of the intervention is performed considering intervention data gathered in real-time during the intervention and the best practices. The intervention is one of construction or maintenance and the execution parameters include a choice of material for R2, a construction technique for R2, a maintenance technique for R2, a design choice for one or more structures of R2, design data related to a design phase of R2.