Automate Infrastructure Inspections with AI! 5 Ways to Achieve a 30% Reduction in Maintenance Costs

Civil infrastructure (roads, bridges, tunnels, water and sewage systems, etc.) built across Japan during the high economic growth period is aging, and its maintenance and management have become major challenges. If regular inspections and repairs are not carried out properly, they can lead to serious accidents, but securing the manpower and budget for inspections has become difficult. With a shortage of experienced engineers and limited budgets, the question is how to streamline inspection work to protect infrastructure safety.

One promising approach to these challenges is the use of AI (artificial intelligence) technologies. Efforts to automatically detect damage to bridges and roads using AI, and to automate inspection work by combining AI with drones and sensors, have begun in many places. By digitizing and reducing the labor of inspections that were traditionally done by human visual checks, it is becoming possible to both improve inspection accuracy and reduce maintenance costs by more than 30%. This article explains five concrete methods to automate civil infrastructure inspections with AI and achieve a 30% reduction in maintenance costs.

• Automatic damage detection by AI image diagnosis

• High-altitude and wide-area inspections using drone × AI

• Continuous monitoring with IoT sensors and anomaly detection algorithms

• Structural understanding via AI analysis of 3D point cloud data

• Efficient inspections through cloud integration and remote decision-making

Now, let’s look at the effects, use cases, and cautions for each method in order.

1. Automatic damage detection by AI image diagnosis

This technology uses AI to analyze images and automatically detect cracks and corrosion of steel materials on the surfaces of bridges and tunnels instead of humans. Cameras capture the surfaces of concrete structures, and deep learning identifies tiny cracks and discoloration in the images with high accuracy. By having AI replace the skilled technicians who used to visually detect areas of deterioration, this contributes greatly to labor saving in inspections and to preventing oversights.

Representative deterioration phenomena detectable by AI image diagnosis:

• Concrete cracks (capable of distinguishing microcracks about 0.1 mm (0.00 in) in width)

• Rust on steel members such as bridges and sluice gates (identifying locations and areas of surface rust and coating peeling)

• Water leakage stains and discoloration on tunnel and dam walls

• Pavement cracks and rutting, etc.

AI image diagnosis technology has already reached practical levels of accuracy, and there are cases achieving detection rates of 90% or higher. For example, NTT Group’s “[Sabi/Hibi Detection AI](https://www.nttedt.co.jp/edrone-ai)” can detect rust and concrete cracks from bridge images with 95% accuracy. Also, tools such as Fujifilm’s infrastructure image diagnosis service “[Hibimikke](https://www.fujifilm.com/jp/ja/business/inspection/infraservice/hibimikke)” can batch-analyze large numbers of drone- or camera-shot photos to automatically mark damaged areas and generate reports. This greatly reduces the time spent organizing data and preparing reports, streamlining post-inspection administrative work.

導入効果: By introducing AI image diagnosis, you can check a vast number of inspection photos in a short time, greatly reducing the time compared to having humans inspect each photo one by one. Since AI can detect minute deterioration that humans might overlook, it leads to preventive maintenance through early detection. Especially as inspection frequency increases, AI can perform initial screening so engineers can focus on areas flagged by AI, contributing to saving human resources and supporting skills transfer.

注意点: However, AI judgment accuracy depends on the quality of the captured images and the training data, so it cannot completely replace human judgment. Misdetections or missed detections can occur depending on lighting conditions or how dirt adheres, so a double-check system where experts perform final evaluations is desirable. Still, using AI-extracted information beforehand will greatly reduce expert workload. Continuous training and updates of AI models are also necessary, and it is important to operate with feedback of new data obtained in the field to improve accuracy.

2. High-altitude and wide-area inspections using drone × AI

Using drones (unmanned aerial vehicles) is effective for inspecting high and large-scale structures such as bridges and dams. By aerially photographing structures with cameras mounted on drones and analyzing those images with AI, deterioration in places where people cannot directly enter can be safely detected. Replacing close-range visual inspections that previously required high-altitude work vehicles or scaffolding with drone flights and AI image analysis can greatly reduce time, cost, and risk.

Main merits of drone × AI inspections:

• Cost reduction: Greatly reduce costs for heavy equipment such as high-altitude work vehicles and scaffolding, costs for traffic control, and personnel expenses.

• Time savings: Drone photography and AI analysis significantly shorten the time required for on-site work and data analysis compared to traditional methods.

• Improved safety: Assigning inspections of high or narrow areas to machines reduces the risk of workers operating in dangerous locations.

For example, in Hokkaido, drone photography and AI image diagnosis were introduced for bridge inspections to perform detailed surveys of bridge undersides without using high-altitude work vehicles. This method made it possible to detect extremely fine cracks less than 0.1 mm (0.00 in) in width that could be missed by conventional manual inspections, achieving precise inspections in a short time. Also, because scaffolding was unnecessary, the time for traffic restrictions could be shortened, reducing the impact on road users. In Fujifilm’s case, analyzing bridge images shot by UAV drones with the AI service “Hibimikke” shortened on-site work periods and achieved approximately 30% cost reductions in some reported cases.

導入効果: Drone × AI inspections can efficiently cover large areas of infrastructure, allowing inspections that would take days by human effort to be completed in hours. This enables both increased inspection frequency and cost reduction, facilitating early detection and response to anomalies. Especially for bridges in mountainous areas or large dams where thorough human access is difficult, drones can approach from above and from the sides to obtain detailed observation data. AI analysis of those data helps standardize inspection accuracy, shifting judgments that relied on veterans to data-driven decision making.

注意点: When introducing drone inspections, compliance with aviation laws and other regulations and the development of safe flight plans are indispensable. Flying over populated areas or over roads requires permission applications, and pilots must have certain skills and qualifications. Weather constraints such as inability to fly in strong winds or rain mean scheduling must be carefully considered. Additionally, systems for managing and storing the large volumes of images and video data obtained by drones must be prepared. These issues can be addressed by outsourcing drone operations to specialized companies or by centrally managing data on cloud services. With proper planning and operations, introducing drone × AI inspections can greatly improve on-site efficiency and safety.

3. Continuous monitoring with IoT sensors and anomaly detection algorithms

This method monitors infrastructure around the clock using IoT sensors to detect abnormalities early, rather than waiting for humans to find them during periodic patrols. Sensors (accelerometers, strain gauges, inclinometers, pressure gauges, etc.) are installed on bridges, tunnels, slopes, etc., and the acquired data are analyzed by AI to detect signs different from normal conditions and trigger alarms. AI anomaly detection algorithms learn normal patterns and automatically notify when vibrations or displacements exceed thresholds, enabling early detection and response even if patrol personnel are not on site.

In practice, systems are offered as remote monitoring services for bridge piers, slopes, dams, and so on. Sensors continuously measure slight tilts of bridge piers or the opening of cracks, and systems that detect minute displacements on the order of 1/100 mm (0.00 in) instantly notify managers by email have been put into practical use. This allows capturing early signs of abnormality that human patrols might miss and planning repairs before serious damage occurs. Continuous monitoring with fixed-point sensors is also useful for emergency inspections during typhoons or earthquakes. For example, installing water pressure sensors on river levees enables real-time warnings by detecting abnormal seepage or leakage during rising water levels, helping to mitigate disaster damage.

On the other hand, there are also methods that utilize sensors mounted on moving vehicles. In Sapporo City, a demonstration experiment fixed a smartphone to the dashboard of a road patrol vehicle to automatically detect road surface irregularities from vibration data collected while driving. Trials have also been conducted to analyze road surface images captured by vehicle-mounted cameras with AI to detect cracks and rutting. As a result, work that used to take five years to inspect all city residential roads could obtain road condition data in just one year, greatly improving efficiency. By combining sensors with anomaly-detection AI, it is possible to collect infrastructure anomaly data during normal vehicle runs, expanding coverage without increasing human burden.

導入効果: Continuous monitoring with IoT sensors captures minute changes that cannot be noticed by human patrol intervals, enabling preventative maintenance-based asset management. Early capture of abnormal signs allows for small repairs that can avoid major accidents or large-scale renovations, reducing total maintenance costs. Also, by maintaining safety while lowering the frequency of regular inspections, this approach helps alleviate personnel shortages. As AI analyzes and accumulates large amounts of sensor data, it learns precursor patterns of failures and deterioration, promising more advanced failure prediction in the future.

注意点: Sensor installation requires initial costs and maintenance expenses (battery replacement, communication costs, etc.), so a strategy to select important locations that need monitoring is necessary. Alerts from sensors are not always true anomalies; false detections due to noise or temporary disturbances can occur. AI detection algorithms must be properly tuned with site-specific environmental data. Additionally, after deployment, attention must be paid to sensor or communication device failures, and periodic calibration and system health checks are important. Nonetheless, the benefits of introducing anomaly detection technology are significant, and more affordable and easier-to-install sensors and wireless communication-based low-maintenance monitoring networks are expected to spread.

4. Structural understanding via AI analysis of 3D point cloud data

A method has also emerged to digitally record entire bridges and tunnels as 3D point cloud data and have AI find anomalies within them. Using laser scanners or photogrammetry, the shape of structures is acquired as point cloud data (a collection of many coordinate points), and AI analysis enables three-dimensional deterioration assessment that cannot be obtained from conventional 2D photos.

Main use cases expected from 3D data analysis AI:

• Automatic detection and measurement of cracks: Automatically extract and measure crack location, length, width, and density from image textures accompanying the point cloud and high-density laser points.

• Deformation assessment: Overlay obtained point clouds with past data or BIM/CIM models from design to extract shape changes such as deflection, settlement, or member displacement.

• Visualization of deterioration progression: Compare periodically acquired point clouds and display the degree of deterioration using color coding to intuitively grasp aging changes.

• Enhanced reporting: Mark deterioration on a 3D model for sharing, making spatial explanations that were difficult with paper reports easy.

• Use in repair planning: Reflect inspection data directly into 3D design drawings (BIM models, etc.) to assist in considering repairs/reinforcements and in quantity calculations.

For example, in port facility inspections, efforts are advancing to generate 3D models of structures photographed by underwater drones in place of divers’ visual checks and use AI to detect cracks. AI links detected crack information to corresponding locations on the 3D model so everyone can confirm the same results, enabling objective inspections. By overlaying past inspection results on a 3D model, it becomes easy to track the history of deterioration over time and see which cracks have existed since when. Presenting inspection results in 3D rather than on paper helps owners and on-site personnel understand spatial situations more easily, shifting communication from “reading reports” to “looking at the model together.” In practice, reports using 3D point clouds and AI made the locations and scales of deterioration immediately evident, improving stakeholders’ sense of understanding and trust.

導入効果: The advantage of 3D point cloud analysis is that it can digitally record the entire structure without omission. Information that was previously expressed fragmentarily with paper drawings and photos can be integrated on a 3D model to share deterioration conditions intuitively. Because AI extracts deterioration parts from enormous point cloud data, engineers can concentrate on evaluating important anomalies, reducing oversights. Also, once acquired, point cloud data remain as assets and can be used for future construction planning or additional surveys. Comparing point clouds from periodic inspections to analyze long-term deterioration trends enables more planned maintenance (predictive maintenance). Furthermore, new applications such as virtual inspection training and simulation of repair procedures using 3D data are expanding the use cases.

注意点: Acquiring and analyzing 3D point clouds requires specialized equipment and high-performance computers, so on-site operations involve certain costs and skills. Point cloud files obtained with laser scanners or high-resolution cameras are very large, and setting up cloud-based data sharing and processing environments is also a challenge. However, efficient surveying methods using drone-mounted LiDAR and mobile mapping systems are becoming more common. In addition, technologies that allow easy acquisition of high-precision point clouds by combining smartphone-embedded LiDAR sensors and high-precision GNSS have emerged, lowering the barriers to 3D inspections. While expert support may be needed initially, once the workflow is established, 3D point cloud × AI analysis becomes a very powerful infrastructure maintenance tool.

5. Efficient inspections through cloud integration and remote decision-making

To make the most of AI and IoT-based inspections, it is important to integrate data on a cloud platform so that necessary people can access it at any time. Storing photos, point clouds, and sensor data acquired during inspections in the cloud eliminates the need to transfer USB drives or paper forms between the field and the office. Field engineers can share images photographed on tablets to the cloud on the spot, and headquarters’ experienced engineers can immediately check AI analysis results to make remote judgments and issue instructions in real time. By creating a cloud database integrated with geographic information systems (GIS), you can link historical inspection data and drawings to each infrastructure asset and use it as a digital ledger for long-term maintenance management.

The merit of cloud integration is that it allows sharing expertise beyond time and place constraints. For example, even if a local government lacks experienced engineers, remote specialists or retirees in other regions can diagnose inspection data via the cloud and provide advice. In practice, remote collaboration, such as sharing concrete structure inspection data on the cloud and discussing repair methods while confirming deterioration status in real time with designers and contractors, is spreading. Also, multiple people can review AI automatic analysis results on the cloud simultaneously, enabling smooth double-checking to prevent oversights and misjudgments. When all parties involved in inspection work can view the same data during discussions, decision-making accelerates and consensus building becomes more efficient.

Moreover, cloud use reduces the burden of report creation and administrative work. Previously, inspection results needed to be reorganized in Excel or on paper, but if data are organized and visualized on a cloud system, it is easy to automatically generate reports and perform statistical analysis. For example, AI automatic markings on inspection photos can be reviewed and corrected on the cloud and then output directly into report formats. Such digitization improves administrative efficiency and helps prevent human errors.

導入効果: Introducing cloud and remote decision-making mechanisms reduces physical travel and mailing times and dramatically speeds up the process from inspection to decision-making. In emergencies requiring urgent inspections, sharing on-site conditions via the cloud enables rapid consideration of countermeasures and prevents delays in initial responses. Also, analyzing accumulated big data with AI makes it possible to perform high-level management, such as analyzing overall infrastructure deterioration trends and predicting high-risk locations. Knowledge accumulated on the cloud becomes an organizational asset, and data handover when personnel change supports skills transfer and elimination of dependence on individuals.

注意点: Ensuring information security is essential when using the cloud. Inspection data may include detailed information about critical facilities, so proper access controls, encryption, and backup systems must be implemented. If a network environment (mobile communication, etc.) for accessing the cloud from the field is not available, the system cannot be fully utilized. In mountainous areas or inside tunnels, consider operating so that data are stored offline and uploaded later. Training and education for field personnel on new system introductions are also important. However, once the convenience of cloud integration is experienced, many report that they cannot return to analog work because efficiency improves so much. As DX (digital transformation) in infrastructure maintenance progresses, cloud and remote decision-making will become indispensable elements.

Conclusion: Further efficiency gains with AI inspections and LRTK

We have looked at how five AI-based approaches can streamline and enhance civil infrastructure inspections. In addition to these advanced technologies, the recent emergence of LRTK (a simple surveying device that combines smartphones with high-precision GNSS) is another new tailwind for infrastructure maintenance. LRTK is a solution that attaches an ultra-compact RTK-GNSS receiver to a smartphone such as an iPhone, enabling one-handed centimeter-class positioning and point cloud measurement. Developed by a venture from Tokyo Institute of Technology, it is pocket-sized weighing only approximately 125 g and achieves positioning accuracy of several cm (several in) in standalone positioning and sub-1 cm (0.4 in) in averaged positioning. By pressing a positioning button, latitude, longitude, and altitude are recorded automatically and the data are immediately reflected and shared on a cloud map, enabling anyone to perform surveying and recording easily with one smartphone per person.

LRTK alone improves on-site operations, but combined with AI image analysis and point cloud analysis it becomes an even more powerful tool. For example, photos taken with a smartphone + LRTK can be fed into AI image diagnosis to attach accurate position coordinates to automatically detected damage. This enables automatic creation of reports that show crack locations precisely on a map. Also, scanning structures with an LRTK-compatible smartphone to obtain point cloud data and extracting deterioration with AI analysis can realize low-cost 3D inspections that previously required expensive equipment. In fact, initiatives have begun to combine positioning information from LRTK with point clouds acquired by smartphone cameras and LiDAR to generate high-precision 3D models in a short time. This fusion of field measurement and AI analysis is expected to further improve inspection accuracy and efficiency.

The field of infrastructure maintenance is now at a major turning point. To address aging and manpower shortages and protect safety with limited resources, it is essential to proactively adopt new technologies such as AI, drones, IoT, and LRTK. By combining these technologies appropriately, a 30% reduction in maintenance costs is by no means a dream. Let’s smartly transform inspection operations with technology and build resilient infrastructure that we can confidently pass on to future generations.

For more details on LRTK, please also refer to the [official site](https://www.lrtk.lefixea.com) published by the developer, Lefixea Inc.