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An Automated Road Surface Inspection Using Machine Learning: A Game Changer for Road Asset Owners

The Importance of Proactive Pavement Maintenance in Optimising Your Road Asset Management Strategy and Therefore Your Available Budget

Automated Road Surface Inspection Using Machine Learning: A Game Changer for Road Asset Owners - Maintain-AI
Automated Road Surface Inspection Using Machine Learning: A Game Changer for Road Asset Owners

Automated road surface analysis using machine learning has become a game-changer for road asset owners who need to manage their networks with limited resources. This new approach replaces traditional road inspection practices or road network analysis, which are labour-intensive, time-consuming and often inefficient. Road asset owners who embrace this technology can optimise their resources, improve safety, prolong pavement life and can considerably reduce the total lifecycle cost of road ownership.

When Working With a Limited Asset Maintenance Budget, the Goal is to Maximise the Value of Each Dollar. This is How Maintain-AI Aims to Support Road Asset Owners.

Maintain-AI is an innovative company that offers an automated road surface inspection system that utilises machine learning to rapidly inspect road surfaces and provide road data analytics. Our AI solution provides numerous benefits, including consistency of defect and object detection, reliability with a high rate of repeatability, efficiency in increasing productivity of network-level pavement evaluations, economical in comparison to other alternatives and scalability in capturing the condition of pavement distresses and other attributes across the road network.

Step 1: Understanding Where You are Today with Automated Road Surface Inspections

To optimise a road network, road asset owners need to have access to the right road analytics and associated information at the right time which requires knowing the current state of their roads as a starting point. The use of time-consuming, labour-intensive traditional methods that survey a certain percentage of roads annually or evaluate a general sample of their network are still common, but they significantly underestimate the true health of current road conditions.

Automated road surface inspections using machine learning, however, can provide a more consistent and broader assessment of pavement conditions and existing deterioration, which allows for better forecasting and measurements of network condition progress. With this technology, road asset owners can, from the safety of their vehicles, more frequently inspect their roads for significantly less budget than traditional methods and collect and classify more data on:

  • Surface deteriorations (e.g. bleeding),

  • Surface deformations (e.g. rutting),

  • Cracks (e.g. alligator),

  • Structural problems (e.g. potholes) and

  • Other network associated issues (e.g. line marking, traffic signs, rider comfort etc.).

By utilising machine learning technology that uses computer vision, road asset owners can therefore more consistently identify the condition of their road network, identify more suitable treatments at the right time and support a robust pavement management strategy that integrates with predictive ability, allowing decision-makers to understand the impact of their choices today on the network tomorrow.

To support road asset owners make more informed decisions about where to best allocate maintenance funds, Maintain-AI offers the most cost effective road inspection solution that can quickly and consistently provide results regarding the condition of the roads as well as identify trends as they develop. The type of distress identifies the kind of damage that has developed, the severity describes how severe the damage is and the quantity describes the extent of the type and severity of damage that is present. All three of these factors are required in at least some capacity to get an increased understanding of the damage that has developed on the pavement surface and thus can be used to determine and optimise the type and timing of maintenance, rehabilitation and/or reconstruction.

Artificial intelligence in Road Maintenance and Asset Management: Data Collection Made Simple

Maintain-AI’s data collection process is a simple and intuitive approach that only requires a smartphone[1] device and minimal equipment. The purpose of data collection is to collect pavement condition photos and other smartphone data for the entire pavement network and surrounding infrastructure.

Maintain-AI offers reliable results to support evidence based decisions of the pavement network and help the community have safer roadways.

By adopting Maintain-AI’s methodology, there is no additional need to take notes on road conditions while driving or to exit the vehicle to inspect the majority of distresses. The resulting video footage captured (obtained through the Maintain-AI App) is analysed by our Machine Learning AI algorithms and presented within a very intuitive and user friendly web-browser based dashboard. The data collected by Maintain-AI is also made available in industry-standard formats.

[1] Maintain-AI can potentially analyse images and data from other devices providing the quality of the source image is suitable and there is accurately registered GPS information associated with the collected data.

Where could AI-based road inspections help you? Explore the potential of AI in road maintenance with our evaluation tool; a data-driven approach to understanding and optimising your infrastructure strategies. Try it here.

Step 2: Developing Your Strategy for ‘Keeping the Good Roads Good’

Once road asset owners understand their network's current health condition, the next step is to develop a strategy for "keeping the good roads good". The strategy should focus on proactive maintenance, which is essential to help extend the life of a pavement and minimise more costly delayed repairs.

Regular Road Surveys Can Track Changes in Condition Early, Allow Early Intervention and Prevent Future Expensive Remedial Work.
Road Authorities Are Often Informed By The Public When a Road's Performance is Below Community Standards - Maintain-AI
Road Authorities Are Often Informed By The Public When a Road's Performance is Below Community Standards

To develop a successful pavement management strategy, road asset owners need to take advantage of more frequently collected data that can be beneficial in decision making, such as strategies supporting Equivalent Annualised Cost (EAC), Remaining Service Life (RSL) and Cost-Benefit Value (CBV) assessments. Integrating these concepts with a predictive ability to justify funding needs to decision-makers and assigning budgets where it matters most will lead to significantly better life cycle cost ownership of road infrastructure and - keep the good roads good.

With the automated road surface inspection technology offered by Maintain-AI, road asset owners can collect more frequent data, which enables them to optimise their treatment plan and develop metrics that can be more beneficial in decision-making.

Step 3+: Continuous Improvement and Using Machine Learning to Provide Even Better Outcomes

An Automated road surface inspection system, using machine learning, also facilitates continuous improvement by allowing road asset owners to measure their progress. By analysing the data collected over time, road asset owners can understand how their treatment plans perform against expectations, identify areas of improvement and develop added strategies for even greater gains.

Just as a car's dashboard provides the driver with real-time information on the car's condition, automated road surface inspections using machine learning provide road asset owners with almost real-time data, enabling them to take a proactive approach to maintenance, understand the impact of their choices today on the network tomorrow and make the most cost-effective and robust choices for short and long-term impact.

Machine learning also provides road asset owners with better outcomes by identifying anomalies and predicting road conditions, enabling them to take a proactive approach to maintenance. With predictive analytics, road asset owners can identify potential problems before they occur, leading to increased safety, reduced costs and longer pavement life.

In Summary

Automated road surface inspections using machine learning, as offered Maintain-AI, are revolutionising the way road asset owners manage their networks.

AI inspections provide a simple and effective approach to network optimisation, allowing road asset owners to make smart choices for short and long-term impact.

By utilising automated road surface inspections, road asset owners can understand where they stand today, develop strategies for keeping good roads good and continuously improve their outcomes.

The solution offered by Maintain-AI is:

  • Effective in its AI object detection capabilities when compared to a reference baseline,

  • Scalable as Maintain-AI captures the condition of pavement distresses as well as other attributes across the road network from the same collected image data set,

  • Reliable in qualitatively identifying defects with a high rate of repeatability,

  • Efficient in increasing productivity and network-level pavement evaluations; and

  • Economical in comparison to other alternatives.

The technology also allows for more frequent data collection, leading to more consistent and cost-effective assessments of pavement conditions. Maintain-AI also has the potential to facilitate predictive analytics, identifying potential problems before they occur and enable proactive maintenance, ultimately resulting in increased safety, reduced costs and longer pavement life.

If money is short – and it usually is – there's only one rational course of action: - maintain existing roads before funding new ones. - inspect your roads more often to make better decisions. - make sure it is done today and even every day. Because tomorrow, it will be much more expensive. (Piarc – World Road Association)

To make the most of their resources and improve the overall condition of their road networks, it is crucial for owners of road assets to adopt automated road surface inspections using machine learning, even if it means using this technology within a "hybrid" strategy with conventional road inspection techniques. The best road infrastructure at the most affordable price should be provided for end users. They deserve it.

Maintain-AI is on a journey to support road authorities make more informed decisions but we only have some of the solutions. Let's work together.

Remember, good roads should cost less.

Let us know your thoughts?

Drive safely;

About Maintain-AI:

Maintain-AI aspires to support Governments, other Road Asset owners and Industry professionals transform pavement and network assessments through AI-driven solutions. Founded on the principle that "Good Roads Should Cost Less", we harness the power of computer vision and machine learning to automate road surface inspections. Our state-of-the-art tools detect road defects and assess related infrastructure, enabling professionals to make data-driven decisions. By advocating for the optimal use of maintenance budgets, we emphasise that well-maintained roads are more cost-effective across a road's complete asset lifecycle. Our commitment is to support regular, objective network inspections, ensuring that every maintenance dollar is maximised. With Maintain-AI, infrastructure asset management is not only efficient but also offers a clear return on investment through maintenance savings. Join us in our mission to make roads better, safer, more sustainable and more cost-effective. All road users deserve it.

digital road inspections

digital asset management

digital transformation


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