Why Investing in Preventive Maintenance and Using the Latest Automation Technology Should Be the Top Priorities in a Road Maintenance Strategy.
Introduction to Today
Road maintenance is a crucial aspect of transportation infrastructure management. Proper maintenance ensures that roads remain safe and functional, enabling efficient movement of goods and people. Yet, maintaining roads efficiently to maximise the value of each dollar spent presents a number of obstacles; as a result, creative solutions are required to enable the step change needed to more effectively use money, time and resources. In recent years, automated road survey systems and asset management systems have emerged as solutions to address some of the most pressing challenges.
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Challenges of Road Maintenance
Maintaining roads is a complex and costly endeavour and presents numerous challenges. One of the most significant is the sheer size and complexity of road networks, which can span hundreds or thousands of kilometres. This makes it difficult to identify and prioritise maintenance needs efficiently.
Based on the sheer size challenges brings another critical concern to road maintenance, and that is, the limited funding available for repair and maintenance activities. In many cases, road agencies struggle to secure the necessary funds to address the growing maintenance backlog, leading to the continued deterioration of the road network's condition. Another challenge is the limited resources available for conducting routine inspections and assessments, which can lead to the delayed identification of maintenance issues and thus lead to subsequently increased repair costs.
Road maintenance is also affected by environmental factors, such as temperature fluctuations, precipitation and natural disasters like floods and earthquakes. It has become increasingly difficult to gather road information using the conventional methods on a regular enough basis to keep roads maintained in good condition and this is why the broader use of technology is desperately required to make a positive step change to how our most important infrastructure assets, our roads, are managed.
Automated Road Survey Systems and Asset Management Systems
Automated road survey systems and asset management systems are technologies that can help address some of the key challenges of road maintenance. Automated road survey systems, such as those that gather and analyse extensive data on road conditions using computer vision and machine learning through artificial intelligence (AI), are proving revolutionary for today's best practise inspection approaches and supporting road safety improvement. This data can be used to identify maintenance needs timely and consistently, prioritise repairs and plan maintenance schedules more efficiently. Asset management systems then use this data on road conditions and other factors to create a comprehensive inventory of road assets. This allows transportation agencies to make more informed decisions about maintenance and investments, ensuring that all resources are used effectively.
In this article, we explore the benefits and challenges of current road maintenance practices and how using AI-driven ML computer vision technology in automated road survey systems and road asset management provides many benefits. We examine the various types of data that can be gathered, the methods of data analysis and monitoring and the solutions that offer efficient and consistent road condition and inventory services. We also discuss the importance of collaboration with local and state road agencies, the ease of data collection, and the accessibility of data across various platforms. By utilising the capabilities of AI and computer vision technologies, road authorities will be supported in making more informed and timely decisions regarding the needs for road maintenance intervention and better utilise existing budgets for the best possible outcomes of road users.
The High Cost of Current Maintenance: Why Automated Road Surveys Are Essential
The cost of maintaining roads is a major concern for governments and communities worldwide. Maintenance costs not only include repairs but also economic losses due to traffic congestion and accidents caused by poor road conditions. Recent statistics show that poor road maintenance leads to decreased productivity and increased vehicle operating costs. However, with the advent of automated road survey systems, the process of road maintenance cans become more consistent, faster and cost-effective.
Road condition surveys are an essential part of asset management and require the measurement and analysis of road data to determine road conditions. Traditional road surveys involve two (2) main methods:
1. A survey vehicle measuring road conditions through visual assessment and data collection.
This process is time-consuming and prone to human errors, which results in limited resources for the maintenance of vast road networks.
2. Using Automatic Road Analyser (ARAN) vehicles or ROMDAS vehicles equipped with lasers and other sophisticated sensors.
Whilst accurate, these systems are very expensive and difficult to secure thus are used far too infrequently to provide any consistent benefits as road conditions can deteriorate quickly between inspections which adds to the lifecycle costs and missed opportunities of improving pavement maintenance.
On the other hand, automated road surveys that make use of Artificial Intelligence (AI) technology in the form of computer vision and machine learning-led automated road inspection systems guarantee that the process of inspecting road surfaces and surrounds is quick, dependable and above all, economical. These systems consist of a range of features that combine measurements of cracks, potholes and pavement deterioration, among other factors, to provide a consistent evaluation of a road network’s health state.
Currently asset managers lack accurate and consistent methods to support decision-making on maintenance programmes resulting in an ad-hoc approach to deciding which areas of road to repair and maintain. A predictive and city-scale maintenance approach based on accurate information would allow more efficient planning, reducing the cost of works and disruption.
And although they are typically less precise than laser scanning systems, AI-based pavement inspection solutions can nonetheless offer a lot more benefits as a holistic solution since they permit much more frequent assessments with enough detail to still provide road agencies the flexibility to schedule maintenance and repair work at the best time possible, hence lowering the total cost of pavement management as light preventative treatments are preferable to heavy treatment in best practise pavement maintenance strategies.
Other advantages of automated road surveys that possess a computer vision and machine learning-led focus include:
The technology ensures that the data collected is reliable and in a user-friendly format
The technology provides complete data transparency, which enables road authorities to make effective decisions based on the collected data.
The technology is developed to detect and classify defect types such as cracking, potholes, rutting, bleeding and other types of deterioration.
The technology can also be used for long-term tracking and monitoring of road conditions, taking into account environmental factors such as climate change.
Equipment required for data collections is as simple as the latest good quality mobile phone.
These benefits are important as keeping roads in good condition requires the early detection of road deterioration to identify the most optimal time to apply the most appropriate treatment that leads to the most cost-effective preventative maintenance application. Good roads should cost less!
Automated road surveys are not limited to highways and strategic road networks; they are equally essential for local road networks. Local roads typically have lower standards and are more prone to surface disintegration, potholes and cracking. Automated road surveys provide a cost-effective means of assessing the condition of local roads, reducing the number of people complaints and preventing the high cost of maintenance across the road’s service lifetime. Road authorities will have the necessary knowledge to make more informed decisions due to the variety of ways this information may be displayed, including interactive displays, charts and satellite images.
Budget Constraints: Balancing Needs and Resources
As with any public service, road maintenance and highway maintenance requires significant and consistent financial investment. However, budget constraints often create challenges for asset managers who need to balance the needs and resources to ensure that road networks are safe and functional. While road maintenance remains a priority, other essential services such as healthcare, education and infrastructure development will also require funding. The impact of budget constraints on road maintenance can thus lead to difficult decisions about which roads and infrastructure to prioritise for maintenance.
1. Road Maintenance Budget Constraints
As roads deteriorate over time, they require maintenance and repair to remain safe and functional for vehicular traffic. Road condition surveys provide valuable data for road asset managers to make informed decisions about maintenance requirements. In this context, budget constraints pose a significant challenge for asset managers who need to balance the need for maintenance with the resources available. The lack of timely maintenance will lead to significant deferred costs associated with road rehabilitation, which is nearly always more expensive than preventative maintenance.
2. Difficult Decisions - Prioritising Roads for Maintenance
Prioritising roads for maintenance is a complex process that requires consistently collected data on road conditions, traffic and other factors.
Suppose an asset manager has limited funds for maintenance and needs to prioritize certain roads for repair. If road ‘A’ has moderate cracking, and road ‘B’ has severe potholes, both require maintenance, but the asset manager can only repair one. In this case, the asset manager needs to make a difficult but informed decision about which road to repair based on their available budget and the severity of the damage but also other factors such as Average Annual Daily Traffic (AADT), road hierarchy, road location relative to other critical infrastructure, impact to traffic conditions etc. Automated road surveys have emerged as an innovative technology that can support improving this process. Automated road surveys can use mobile phone cameras and computer vision to gather data on road conditions very efficiently and thus provide the ability to understand the health state of road assets and support improving road maintenance and highway maintenance practices very quickly.
3. Overcoming Budget Constraints
The traditional road inspection and data collection process is expensive and time-consuming and it may not always provide representative data. As a result, asset managers should increasingly adopt new technology such as automated road inspections using computer vision and AI to gather data more efficiently and accurately. Despite the efforts of councils, which repair a pothole every 19 seconds, our local roads repair backlog is rising…
With Maintain-AI’s solution, Automated road surveys can be conducted very simply using a normal vehicle equipped with a mobile smart phone device to capture road condition data. The road authority or maintenance provider can then process the data and provide information on rider comfort, cracking, potholes and line marking quality. This system offers road authorities significantly more consistent data, much more affordably than traditional inspection methods and can detect defects that may be missed by manual inspections.
4. Innovative Funding Models
In addition to using technology, asset managers can also explore innovative funding models to overcome budget constraints. Subscription-based products, such as through an automated road analyser or AI damage inspection contracts, can be cost-effective solutions for asset managers to gather data and make effective decisions. The use of Artificial Intelligence (AI) technology and video data will also provide reliable, consistent, objective and representative surface condition reports for asset managers holistically better than any other traditional approach to road surface inspection.
5. Benefits of Adopting New Technology
Automated road inspections using computer vision and AI offer several advantages over traditional inspection methods.
For example, Maintain-AI’s solution supports a data collection process that is fast and efficient, meaning that asset managers can make informed decisions quickly. It also reduces the risk of human errors and the data is available in a user-friendly format that is easy to interpret. The solution also provides complete data transparency where data is tabulated and presented in a secure and interactive display. The output charts are hosted on the Cloud, meaning that there is no need to install software and the data is accessible anywhere across various platforms with a developer-friendly API. Advanced filters, heatmaps and specific parameters can be used to navigate data and provide multi-year comparisons.
Extending the Life of Roads and Highways: Innovative Solutions for Aging Infrastructure
As countries continue to develop, so does the demand for transportation infrastructure, including roads and highways. However, the challenge of aging infrastructure is becoming increasingly apparent as the maintenance costs continue to rise and safety concerns multiply. Aging roads and highways require frequent repair and rehabilitation, which can be a significant drain on resources.
The Challenge of Aging Infrastructure
Aging infrastructure presents several challenges to road authorities and asset managers. The maintenance process is time-consuming, expensive and can be dangerous to both maintenance teams and the public. Furthermore, inadequate maintenance and inspection of roads and highways can lead to accidents and costly repairs and other liabilities.
Innovative Solutions for Extending the Life of Aging Infrastructure
To address the challenges posed by aging infrastructure, road authorities and asset managers are turning to innovative solutions. One such solution is the use of advanced materials and technologies in pavement construction, such as high-performance asphalt and concrete. These materials provide greater durability, strength and resistance to damage, which reduces the need for frequent repairs and extends the life of roads and highways.
Councils across the country are facing unprecedented increased costs to repair our local roads, keep our street lights switched on and invest in improved local infrastructure. Latest Local Government Association estimates reveal that the average cost of a pothole repair has increased by around 25 per cent….
Another solution is the use of automated road survey systems, which provide accurate and reliable measurements of road conditions. Automated road survey systems such as Maintain-AI use sensor technologies, such as video data, to gather data on road conditions, including cracks, potholes and other types of deterioration. This data can then be used to identify maintenance issues very early in the pavement’s degradation cycle and well before they become more costly and dangerous.
Preventive Maintenance and Regular Inspections: An Intelligent Solution to Improving Local Road Maintenance
Preventive maintenance and regular inspections are essential for extending the life of ageing infrastructure. By identifying and addressing maintenance issues before they become more costly and dangerous, preventive maintenance can save road authorities and asset managers significant resources – time, people and money. Regular inspections can be carried out using advanced tools and technologies, such as automated road inspection systems, to provide consistent, objective and reliable data on the condition of the road network.
The more recent automated road inspection systems use Artificial Intelligence (AI) technology to detect defects such as cracking, potholes, edge breaks and bleeding. The AI algorithms can identify and classify the defect types and provide a detailed and objective surface condition report. This information is crucial for road asset managers to prioritise maintenance and repair works, which can result in cost savings and improved road safety.
Addressing Workforce Shortages in Road Maintenance through Technology-driven Solutions
Road maintenance is an essential service that requires skilled personnel to ensure roads are safe and well-maintained. However, the road maintenance industry is facing a significant challenge, with a shortage of skilled personnel. The shortage of qualified workers impacts the quality and efficiency of maintenance work, leading to higher costs and longer project timelines. Addressing this challenge requires innovative solutions that incorporate technology to attract and retain young professionals in the industry.
The Challenge of Workforce Shortages in Road Maintenance
The impact of workforce shortages is most evident in the quality and efficiency of maintenance work. For instance, limited resources and the lack of qualified personnel to conduct road condition surveys, gather data and analyse performance data leads to inadequate monitoring of road conditions. As a result, potholes, cracking and deterioration go unnoticed, leading to increased costs and delays in maintenance.
Successful Strategies for Addressing Workforce Shortages
Several strategies have been successful in addressing workforce shortages, including training and apprenticeship programs. However, technology-driven solutions have emerged as a highly effective strategy. For example, as their name implies, automated road survey systems, particularly those that adopt machine learning and computer vision, have been developed to measure road conditions automatically, or at least, significantly reduce the burden of time and effort. The simplest solutions consist of a vehicle equipped with a mobile phone camera attached to the windscreen that captures video data on road conditions at normal driving speeds, including cracks, potholes and surface texture. The data collected is then analysed by sophisticated machine learning AI algorithms to assess and score the road's condition, enabling road authorities to make more timely and informed decisions on maintenance requirements. This technology enables objective surface condition reports to be generated, eliminating human errors and improving efficiency.
Attracting and Retaining Talent in the Road Maintenance Industry
Attracting and retaining young professionals in the road maintenance industry is essential. Using technology-driven solutions, such as Artificial Intelligence, can be an effective means of attracting young talent. For instance, technology, particularly Artificial Intelligence (AI), can be an effective strategy for several reasons.
AI can streamline and automate tedious and time-consuming tasks, such as data entry and analysis, freeing up employees' time for more engaging and stimulating work.
AI can help identify and prioritise road maintenance needs, allowing for more efficient and effective use of resources. This can help employees feel like their work is making a real impact to community well-being and contributing to their sense of job satisfaction.
Incorporating AI into training and development programs can provide employees with opportunities to learn new skills and stay up-to-date with emerging technologies, which can be a strong incentive for retaining talent in the industry.
With the excitement around AI and machine learning solutions, it is essential to also harness the wave of youthful exuberance for all things technology to attract and retain talent in the road maintenance industry. Additionally, AI solutions through computer vision in road asset management strategies offer a consistent evaluation of road surfaces, providing insights that are not visible to the naked eye, thus improving road safety for drivers and pedestrians – another positive social motivation for attracting and retaining young talent within the industry.
AI Driven ML Computer Vision: Building Trust and Engagement in Road Maintenance
Maintaining road infrastructure is a crucial component of economic and social development, and the efficient management of this asset is essential to ensure that roads are safe, reliable and accessible. One of the primary challenges faced by road authorities is maintaining a positive public perception of road maintenance and ensuring support for future maintenance projects. The use of AI-driven technology in road maintenance can help support quick and efficient road maintenance practices, building trust and transparency with stakeholders and the public.
How AI is Revolutionising Road Maintenance? Consistent and Efficient Analysis
AI-driven ML computer vision technology provides a reliable methodology and ensures the efficient analysis of collected data. With almost real-time road inspection and same-day delivery of results, this technology enables road authorities to obtain up-to-date data on the whole road network. This efficient analysis of data can help improve road asset management and prevent expensive losses that may arise due to poor road conditions or by reducing maintenance costs by applying the most cost-effective treatment for a particular damage at the most optimal time.
AI-driven ML computer vision technology enables road authorities to provide complete data transparency to stakeholders and the public. By providing this information in a user-friendly format, similar to Google Maps, stakeholders can easily navigate data and access detailed information about the road condition, its exact location, road name, road number and latitude/longitude. The data can be presented in different formats, such as output charts, heatmaps and specific parameters. Customer-defined data layers with attribute values can also be analysed with multi-years of data and side-by-side comparisons.
Effective and Efficient Maintenance
There are two ways to measure the condition of a road network. The first way is to call it the Squeaky Wheel, sit back and wait for the complaints. The more complaints, the worse the condition of the roads. The second way is to use a more thorough, comprehensive and pro-active approach to review the entire road network.
OGRA’S MILESTONES | V9#4 | www.ogra.org
Maintain-AI uses AI-driven ML computer vision technology to help road authorities make effective and efficient decisions regarding road maintenance. The technology acts as a positive solution to pre-empt and identify problematic road conditions before they become larger more publicly visible problems assuring public users that proactive actions are taking place consistently that meets or exceeds industry standards and their requirements.
Data Management: Leveraging Technology for Better Maintenance
Effective road maintenance is critical for ensuring road user safety and comfort, economic development and social welfare. ‘Keeping the Good Roads Good’ is a strategy of maximising the value of each dollar invested in road maintenance to improve performance and asset lifecycle costs. To accomplish this purpose, road authorities must have reliable and timely data regarding the health condition of their road networks. Automated road survey systems, also termed automated pavement surface assessments, automated road data collection and artificial intelligence (AI) technology, is transforming road maintenance by allowing for quick, consistent and cost-effective data collection, analysis and decision-making.
Automated Road Survey Systems
Automated road survey systems use a combination of technology, such as cameras, GPS and video data to gather data about the condition of the road surface. These systems eliminate human errors, ensure the repeatability and reproducibility of the data and provide a reliable methodology for data collection.
Automated road analysers that use AI damage detection systems for example, also use machine learning algorithms to classify and detect different defect types and provide an objective surface condition report. The data obtained from these systems helps road authorities determine the essential maintenance and repair work required and provides the ability to better allocate their limited resources more efficiently, but most importantly, at the right time.
Data Consistency and Quality
Data consistency and quality are essential for effective road maintenance. Automated road survey systems can provide complete data transparency, interactive displays, and developer-friendly APIs, allowing road authorities to navigate the data and monitor the road condition in almost real-time.
Automated road surface assessments using AI within road condition surveys can detect and approximate the severity and extent of different defects, creating a comprehensive database of road conditions. Moreover, the use of AI technology can enable same-day data collection and delivery of results, improving the efficiency and effectiveness of road maintenance. Asset management systems can also use this data to develop pavement deterioration curves to make more learned approximations of potential future pavement performance.
Innovative Technology Solutions
Several innovative data analysis options can be used to provide additional analytical strength to automated road survey systems and generated road dilapidation surveys. Predictive analytics and machine learning algorithms can estimate maintenance requirements, optimise resource allocation and assess the impact of environmental and climatic change on the road network.
These innovations are also able to present a range of features and benefits for road authorities through detailed mapping to support knowledge sharing, roadside inventory assessments, road safety improvement and time-stamped historical road performance insights.
Therefore, having a video / image record of a road network through a consistently collected data strategy provides many options to analyse and generate useful insights for road asset owners. With increased information brings enhanced awareness and more informed data points to improve decision making. There really are only advantages with adopting machine learning computer vision led technology in road asset management.
Summary - Optimising Road Maintenance with AI
The high cost of maintaining roads is a major concern for governments worldwide.
Poor road maintenance leads to decreased productivity, increased vehicle operating costs, traffic congestion and road safety issues that can often be attributed to accidents. Automated road survey systems and asset management systems are technologies that can help address some of these key challenges of road maintenance.
Automated road survey systems can very quickly gather and analyse copious amounts of data on road conditions using computer vision and machine learning through artificial intelligence (AI), identifying maintenance needs timely and consistently, prioritising repairs and planning maintenance schedules more efficiently. Optimising road maintenance with AI based automated road surveys that make use of AI technology in the form of computer vision and machine learning-led road inspection systems almost guarantee that the process of inspecting road surfaces and network surrounds is quick, dependable, economical, and above all, safe.
Asset management systems can use this data on road conditions to create a comprehensive inventory of road assets, enabling asset owners and maintainers the ability to make more informed decisions about maintenance and investments, ensuring that all available resources are used most effectively.
Overall, AI-based pavement inspection solutions offer many more advantages than traditional approaches to road surface inspections because they support broader assessments from each data collection programme, giving road agencies the cost-effective consistency to assess their road networks more frequently and thus be in a position to schedule maintenance and specific repair works at the most optimal time possible, lowering the total cost of pavement ownership. Road authorities can now have access to more representative data of actual conditions to understand the true health state of their road networks to make more informed decisions due to the variety of ways this information may also be reported, including interactive visual displays, charts and satellite overlays.
Remember, good roads should cost less! Let's work together.
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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
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