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Digital Road - Maintain-AI

How Does the Maintain-AI Process Work? 

Delivering The Ability to Optimise Your Road Network Inspections and Actions

The continuous inspection of a pavement network is an important element of road maintenance because surface distresses, after initiated, will cause the accelerated deterioration of a pavement's structure.

 

Whilst traditional approaches of distress detection are human-dependent, expensive, inefficient and/or unsafe, Maintain-AI has developed a deep learning AI solution that analyses pavement images at unprecedented accuracies.

Maintain-AI uses the most promising approaches of computer vision algorithms which adopt machine 
learning models to detect the different types and severities of distresses as well as identifying and assessing numerous other associated infrastructure elements from collected road network images.

 

Ultimately, Maintain-AI's AI solution is extremely valuable as we are able to generate results that are considered objective and reliable (minimised standard deviations compared to suitable alternatives), effective (with minimal bias to a suitable reference observed) and efficient (when comparing the resources of cost and people required compared to comparable alternatives).

A Simple But Very Effective Process

The Maintain-AI Process - Data Collection, Analysis and Reporting

Collect Data through your Phone and

Upload to Our Analytics Service

  • Pavement Defect Identification

  • Rider Comfort

  • Network Infrastructure including line markings, signage and more - not just road defects!

  • Customisable Dashboards

  • Defect Severity / Density

  • ePavement Quality Index

  • eRider Comfort Grade

  • ePaser Index

  • Present Serviceability Rating (PSR)

Step 1 - Road Network Data Collection

Scan the Road Network and Upload the Data

Step 2 - Analyse the Selected Data

Use Machine Learning to Evaluate the Results

Step 3 - Reports and Dashboards

Detailed Performance Information of the Network

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Reliable

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Effective

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Efficient

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Scalable

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Economical

  • Time Based Asset Performance Comparisons

  • Treatment Suggestions

  • Implied Repair Costs

  • Cost-Benefit Value Ranking

  • Data / Platform Hosting

  • + More in Development

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