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

Across Japan, civil infrastructure built during the high economic growth era—such as roads, bridges, tunnels, and water and sewer systems—is aging, and maintaining it has become a major challenge. If regular inspections and repairs are not carried out properly, serious accidents can occur; at the same time, securing the manpower and costs required for inspections is becoming more difficult. With a shortage of experienced engineers and limited budgets, the question is how to streamline inspection operations to protect infrastructure safety.

In response to these challenges, the use of AI (artificial intelligence) technology has attracted attention in recent years. Initiatives are beginning nationwide to use AI to automatically detect damage to bridges and roads, and to automate inspection work by combining AI with drones and sensors. By using digital technologies to streamline inspections that were previously performed visually by humans, 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 using AI image diagnosis

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

• Continuous monitoring with IoT sensors and anomaly detection algorithms

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

• Efficient inspections through cloud integration and remote decision-making

Now, let’s look at the introduction effects, use cases, and points to note for each method in order.

1. Automatic damage detection using AI image diagnosis

This technology enables AI to perform image analysis and automatically detect cracks in the surfaces of bridges and tunnels and corrosion of steel materials in place of human inspectors. Cameras photograph the surfaces of concrete structures, and deep learning is used to find minute cracks and discoloration in images with high accuracy. By allowing AI to replace the detection of degradation areas that experienced engineers used to perform visually, this contributes significantly to labor savings in inspection work and prevention of oversight.

Typical degradation phenomena that can be detected by AI image diagnosis:

• Concrete cracks (can distinguish very fine cracks on the order of about 0.1 mm in width)

• Rust on steel members such as bridges and sluice gates (identify the location and area of surface rust and paint peeling)

• Water 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 above 90%. For example, the NTT Group’s “[サビ・ひび検知AI](https://www.nttedt.co.jp/edrone-ai)” can detect rust and concrete cracks in bridge images with 95% accuracy. Also, tools have appeared—such as Fujifilm’s infrastructure image diagnosis service “[ひびみっけ](https://www.fujifilm.com/jp/ja/business/inspection/infraservice/hibimikke)”—that can batch-analyze many photos taken by drones or cameras, automatically mark damage locations, and generate reports as-is. This greatly reduces the time spent organizing data and preparing reports, streamlining post-inspection administrative work.

Implementation effects: Introducing AI image diagnosis allows vast numbers of inspection photos to be checked in a short time, dramatically shortening the time required compared to human inspection of each photo. Since AI can detect minute deterioration that humans might overlook, it also enables preventive maintenance through early detection. In particular, as inspection frequency increases, AI can perform initial screening so that engineers only need to focus on the abnormal areas indicated by the AI, contributing to savings in human resources and support for knowledge transfer.

Points to note: AI judgment accuracy depends on image quality and training data, so you cannot entirely eliminate human judgment. Misdetections or missed detections may occur depending on lighting conditions or surface dirt, so a dual-check system with experts making final evaluations is desirable. Even so, using information automatically extracted by AI in advance greatly reduces expert workload. Continuous learning and updating of AI models is also necessary, and it is important to operate systems that feed new field data back to improve accuracy.

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

Using drones (unmanned aerial vehicles) is effective for inspecting structures that are high or cover wide areas, such as bridges and dams. By aerially photographing structures with cameras mounted on drones and analyzing the images with AI, deterioration in places humans cannot access directly can be safely detected. Replacing close visual inspections that previously required aerial work platforms or scaffolding with drone flights and AI image analysis can significantly reduce time, cost, and risk.

Main advantages of drone × AI inspections:

• Cost reduction: Can greatly reduce costs related to heavy equipment such as aerial work platforms and scaffolding, traffic control, and labor costs.

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

• Improved safety: Assigning inspection of high or confined areas to machines reduces the risk to workers operating in hazardous locations.

For example, in Hokkaido, drone photography and AI image diagnosis were introduced for bridge inspections, enabling detailed investigation of bridge undersides without using aerial work platforms. This method detected even very fine cracks smaller than 0.1 mm—which might be overlooked during conventional manual inspections—and achieved precise inspections in a short period. Because scaffolding is unnecessary, traffic restriction times were also reduced, lessening the impact on road users. In Fujifilm’s case, analyzing bridge images taken by UAV drones with the “ひびみっけ” AI service shortened on-site work periods and achieved about 30% cost reduction in some cases.

Implementation effects: Drone × AI inspections can cover wide areas efficiently, allowing inspections that used to take days by hand to be completed in hours. This makes it possible to increase inspection frequency while reducing costs, facilitating early detection and response to abnormalities. Especially for mountain bridges and large dams where it is difficult for people to access every part, drones can approach from above or the sides to collect detailed observational data. AI analysis of those data helps standardize inspection accuracy, shifting decision-making from reliance on veterans to data-driven processes.

Points to note: When introducing drone inspections, compliance with aviation laws and other regulations and development of safe flight plans are essential. Permissions are required for flights over populated areas or above roads, and operators need certain skills and qualifications. Weather constraints—such as being unable to fly in strong winds or rain—require careful scheduling. Additionally, a system for managing and storing the large volume of images and video data acquired by drones must be prepared. These issues can be addressed by outsourcing drone operations to specialized companies or centralizing data management on a cloud service. With appropriate planning and operation, drone × AI inspections can greatly contribute to on-site efficiency and improved safety.

3. Continuous monitoring with IoT sensors and anomaly detection algorithms

Rather than waiting for humans to discover issues during periodic patrols, this approach uses IoT sensors to monitor infrastructure condition 24/7 and capture anomalies early. Sensors (accelerometers, strain gauges, inclinometers, pressure sensors, etc.) are installed on bridges, tunnels, slopes, and other structures, and the collected data are analyzed by AI to detect signs that differ from normal and trigger alarms. AI anomaly detection algorithms learn normal patterns and automatically notify when vibrations or displacements exceed thresholds, enabling early detection and early response even when patrollers are not on site.

In practice, systems are already provided as remote monitoring services for bridge piers, slopes, and dams. Sensors that continuously measure slight tilts of piers or crack openings can detect microscopic displacements at the 1/100-millimeter level and immediately notify managers by email when such changes are detected. This allows initial signs of anomalies that human patrols might miss to be captured, enabling repair plans to be made before major damage occurs. In addition, fixed-point sensor monitoring is useful for emergency inspections during typhoons or earthquakes. For example, installing water pressure sensors on river levees can detect abnormal seepage or leakage when water levels rise and provide real-time warnings, helping to mitigate disaster damage.

There are also methods that utilize sensors mounted on moving vehicles. In Sapporo, a demonstration experiment fixed a smartphone to the dashboard of a road patrol vehicle to automatically detect road surface irregularities from vibration data while driving. At the same time, road surface images captured by onboard cameras were analyzed by AI to identify cracks and rutting. As a result, what used to take five years to inspect all residential roads in the city could have the road surface condition data collected in just one year, showing substantial efficiency gains. By combining sensors with anomaly-detecting AI, it becomes possible to collect infrastructure anomaly data during existing vehicle runs, expanding coverage without increasing human workload.

Implementation effects: Continuous monitoring with IoT sensors captures tiny changes that cannot be noticed within human patrol intervals, enabling preventive maintenance-oriented asset management. Early detection of anomalies means problems can be addressed with small repairs, avoiding major accidents and large-scale rehabilitation and reducing overall maintenance costs. Also, because safety can be maintained with lower inspection frequency, this approach helps alleviate staff shortages. As AI analyzes and accumulates large amounts of sensor data, it can learn precursor patterns of failures and degradation, raising expectations for more advanced failure prediction in the future.

Points to note: Installing sensors involves upfront costs and maintenance expenses (battery replacement, communication fees, etc.), so a strategy to select critical locations that require monitoring is necessary. Not every alarm from sensors indicates a true anomaly; false detections due to noise or transient disturbances can occur. AI detection algorithms must be properly tuned to each site’s environmental data. Additionally, after deployment, attention must be paid to failures or malfunctions of the sensors and communication devices themselves, and regular calibration and health checks of the system are important. Nonetheless, the impact of anomaly detection technology is significant, and the future will likely see wider adoption of cheaper, easier-to-install sensors and wireless communication networks that reduce operational burden.

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

A method is emerging that digitally records entire bridges and tunnels as 3D point cloud data and uses AI to find abnormalities within that data. Laser scanners or photogrammetry capture the shape of structures as point clouds (collections of numerous coordinate points), and AI analysis enables a three-dimensional understanding of degradation 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 locations, lengths, widths, and densities from image textures associated with point clouds or high-density laser points.

• Understanding deformation: Overlay acquired point clouds with past data or design BIM/CIM models to extract shape changes such as deflection, settlement, or member displacement.

• Visualization of degradation progression: Compare point clouds obtained periodically and display the degree of degradation with color coding to intuitively understand age-related change.

• Enhanced reporting: Mark degradation areas on the 3D model and share them, 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 aid repair/reinforcement planning and quantity estimation.

For example, in inspections of port facilities, efforts to generate 3D models from images captured by underwater drones and have AI detect cracks in place of divers are becoming more active. AI links detected crack information to the corresponding locations on the 3D model, enabling objective inspections where anyone can confirm the same results. By overlaying past inspection results on the 3D model, it becomes easy to track the history of degradation over time—which crack has existed since when—making it straightforward to follow trends. Presenting inspection results in 3D rather than on paper helps owners and site personnel grasp spatial conditions more easily, shifting communication from “reading a report” to “viewing the model together.” In practice, reports using 3D point clouds and AI have made the locations and scale of degradation immediately clear, increasing stakeholder confidence and trust.

Implementation effects: The advantage of 3D point cloud analysis is that it can comprehensively digitally record an entire structure. Information that was previously represented fragmentarily in paper drawings and photos can be integrated on a 3D model, making degradation conditions easy to share intuitively. AI extracts degraded areas from vast point cloud data so engineers can focus on evaluating important anomalies, reducing oversights. Also, once point cloud data are acquired they remain as assets that can be used for future construction planning or additional investigations. Comparing point clouds from periodic inspections to analyze long-term degradation trends enables more planned maintenance (predictive maintenance). Moreover, new applications such as virtual inspection training and simulation of repair procedures using 3D data are expanding.

Points to note: Capturing and analyzing 3D point clouds requires specialized equipment and high-performance computers, and field operation involves certain costs and skills. Point cloud files obtained from laser scanners or high-resolution cameras are very large, so establishing cloud-based data sharing and processing environments is a challenge. However, drone-mounted LiDAR and mobile mapping systems for efficient surveying are becoming more common. In addition, technologies that allow easy acquisition of high-precision point clouds using smartphone-integrated LiDAR sensors and high-precision GNSS are emerging, lowering the barriers to 3D inspections. While expert support may be needed at the outset, once a 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 fully leverage inspections using AI and IoT, it is important to consolidate data on a cloud platform and create an environment where authorized personnel can access it anytime. By storing inspection photos, point clouds, and sensor data in the cloud, there is no need to transfer USB drives or paper reports between the field and the office. Field engineers can share images taken on tablets to the cloud on the spot, and head office experts can immediately review AI analysis results to make remote judgments and issue instructions—enabling real-time collaboration. By creating a cloud database linked with geographic information systems (GIS), inspection data and drawings for each infrastructure asset can be linked and managed as a digital ledger for long-term maintenance.

The advantage of cloud integration is that it enables sharing of expertise beyond time and location constraints. For example, even if a local government lacks experienced engineers, specialists or retired experts from other regions can remotely diagnose inspection data via the cloud and provide advice. In practice, remote collaboration is expanding—sharing concrete inspection data on the cloud and discussing repair methods with designers and contractors in real time while checking deterioration status. Also, multiple people can simultaneously review AI-generated analysis results on the cloud, facilitating smooth double-checking to prevent oversights and misjudgments. When all stakeholders can view the same data during discussions, decision-making speed and consensus-building improve.

Furthermore, cloud adoption contributes to reducing the burden of report creation and administrative work. Previously, results needed to be reorganized into Excel or paper formats, but if data are organized and visualized on a cloud system, automatic report generation and statistical analysis become easy. For example, AI-marked inspection photos can be reviewed and corrected on the cloud and then exported directly into report formats. This digitization improves administrative efficiency and helps prevent human error.

Implementation effects: Introducing cloud and remote decision-making systems reduces physical travel and mailing times, dramatically improving the speed from inspection to decision-making. In emergencies, local situations can be shared via the cloud immediately, enabling rapid consideration of countermeasures and preventing delays in initial response. Moreover, analyzing accumulated big data with AI enables advanced management such as analyzing degradation trends across infrastructure and predicting high-risk locations. Knowledge stored in the cloud becomes an organizational asset, and data continuity even when personnel change aids knowledge transfer and eliminates reliance on individuals.

Points to note: When using the cloud, information security is essential. Inspection data may include detailed information about critical facilities, so appropriate access restrictions, encryption, and backup systems must be in place. Also, if network access (mobile communication, etc.) cannot be secured at the field site, the system cannot be used effectively. In mountainous areas or inside tunnels, operating with offline data storage to upload later should be considered. Training and education for field personnel on new systems are also important. However, once users experience the convenience of cloud integration, many report that they cannot return to the old analog workflows. As DX (digital transformation) in infrastructure maintenance advances, cloud and remote decision-making will become indispensable elements.

Conclusion: Further efficiency gains with AI inspections and LRTK

We have seen that five AI-based approaches can realize more efficient and advanced civil infrastructure inspections. In addition to these advanced technologies, the recent emergence of LRTK (a simple surveying device that combines a smartphone with high-precision GNSS) is providing new momentum for infrastructure maintenance. LRTK is a solution that equips an iPhone or other smartphone with a tiny RTK-GNSS receiver, enabling centimeter-level positioning and point cloud measurement easily with one hand. Developed by a venture from Tokyo Institute of Technology, this pocket-sized device weighing only about 125 g achieves positioning accuracy of several centimeters in standalone positioning and under 1 cm with averaged positioning. With a single press of a positioning button, latitude, longitude, and altitude are automatically recorded and reflected and shared instantly on cloud maps—making surveying and recording accessible to anyone with one smartphone per person.

LRTK alone can streamline fieldwork, but when combined with AI image analysis and point cloud analysis, it becomes an even more powerful tool. For example, photos taken with a smartphone + LRTK and fed into AI image diagnosis can have precise coordinate information linked to detected damage locations. This enables automatic creation of reports that accurately map crack locations. Also, scanning structures with an LRTK-compatible smartphone to obtain point cloud data and extracting degraded areas with AI can realize 3D inspections at lower cost than previously required high-end equipment. In practice, initiatives have begun to combine smartphone camera and LiDAR point clouds with LRTK positioning information 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 undergoing a major transition. To address aging infrastructure and labor shortages and protect safety with limited resources, actively adopting new technologies—AI, drones, IoT, and innovations like LRTK—is essential. Combining these technologies appropriately can make a 30% reduction in maintenance costs far from a pipe dream. Let’s use technology to smartly transform inspection operations and build resilient infrastructure that can be passed on to future generations with confidence.

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