Thorough Examination: Can AI Make Construction More Efficient? Key Points and Cautions for On-Site Implementation

Basic Knowledge of AI

AI (artificial intelligence), which is attracting attention in the construction industry as well, should first be understood at a basic level. AI is a collective term for technologies that replicate and realize human intellectual tasks with computers. In recent years, thanks to advances in machine learning and deep learning, it has become possible to analyze vast amounts of data obtained from images and sensors, learn patterns and rules from them, and make sophisticated judgments. For example, recognizing objects from photos or predicting the future from past data can now be done quickly and with high accuracy. In construction, there are ongoing efforts to use these AI technologies to automate and streamline various on-site tasks. Inspections and planning that previously relied on human experience and intuition are increasingly being supported by AI that provides objective, data-driven assistance. Such AI utilization is expected to be a pillar supporting the construction industry’s DX (digital transformation).

Benefits and Effects of On-Site Implementation

For construction sites facing many challenges—serious labor shortages, long working hours, and low productivity—introducing AI technology can be a trump card for solving these problems. By applying AI to on-site operations, the following main benefits and effects can be expected:

• Improved work efficiency and time savings: Automating tasks such as surveying and inspection allows work to be completed in much less time than before. By reducing manual labor and compressing waiting times and duplicated tasks, the overall workflow speeds up.

• Labor-saving measures to address workforce shortages: In response to chronic shortages of skilled workers, AI can act as a “smart worker.” AI can take over tasks that could not be handled by a small workforce, reducing the number of personnel required while keeping operations running. Also, by learning and sharing the expertise of skilled workers, AI helps with skill transfer.

• Improved safety: Delegating dangerous tasks to AI and machines can reduce the risk of work-related accidents. For example, if drones and AI perform inspections at height, people no longer need to enter hazardous areas. Real-time AI monitoring of video can detect early signs of accidents and issue warnings.

• Standardization and improvement of quality: Using AI in inspection processes eliminates human variability and enables checks against uniform quality standards. AI detects even minute defects, reducing rework and raising construction quality. This contributes to improved customer satisfaction as well.

• Cost reduction: The efficiency gains, labor savings, and safety and quality improvements described above are expected to reduce total costs. Savings on labor through reduced work time, avoidance of wasteful expenditures from fewer mistakes and accidents, and reduced material loss through proper inventory control all contribute to high ROI over the mid-to-long term.

Key On-Site Areas Where AI Can Improve Efficiency

Now let’s look at specific tasks on construction sites where AI can contribute to efficiency. From surveying to safety management, AI is being applied across a wide range of fields. We will examine how AI helps in each area and the current effects.

Surveying (Current Condition Grasp)

Surveying is one of the areas where AI utilization has particularly advanced. Terrain surveying that used to require setting up a total station and measuring point by point manually can now be greatly simplified by combining drones and image analysis AI. Drones capture aerial photos of the site, which AI analyzes to automatically generate 3D point cloud data. This allows rapid understanding of elevation and shape across wide areas—for example, on a forest land development site you can obtain point cloud data for the entire terrain from the air in minutes. People no longer need to climb dangerous slopes or spend days surveying, greatly improving safety and work efficiency. From the acquired 3D survey data, heights and distances of arbitrary cross-sections can be measured later as needed, so AI can automatically perform as-built verification and earthwork volume calculations (cut-and-fill volumes). The Ministry of Land, Infrastructure, Transport and Tourism-promoted i-Construction also emphasizes use of 3D surveying, and surveying technologies using AI have already been put into practical use at many sites.

As-Built Management

As-built management is the process of confirming whether the shape and dimensions of structures or terrain after construction match the design drawings. AI is powerful in this area as well. For example, after embankment or paving work in road construction, survey staff used to measure heights at many points to check as-built conditions, but now 3D scanners or drone imaging plus AI analysis can digitally capture the shape of an entire surface. AI compares the design data with the as-built point cloud and automatically performs as-built inspection, visualizing areas of excess or deficiency with color coding. This enables immediate discovery of areas that need rework, contributing to quality assurance and reduction of rework. As-built records can also be preserved as photos or point cloud data, reducing paperwork and making reporting to clients smoother. AI-based as-built management is progressing especially in civil engineering, achieving substantial efficiency gains on sites that handle large volumes of soil.

Safety Management

AI utilization in safety management at construction sites is also attracting attention. With AI-equipped surveillance cameras, it is possible to monitor the site 24/7, detect dangerous situations, and issue alerts. For example, AI can check from camera footage whether workers are wearing helmets and safety harnesses correctly and immediately notify site supervisors if someone is not. Systems are already in use that sound an alarm if a person or machine is about to enter a restricted area. Research is also advancing on monitoring worker movement and behavior to detect signs that could precede falls or other accidents. These AI-powered real-time monitoring systems can cover moments that human supervisors cannot watch, reducing missed “near-miss” incidents.

Meanwhile, AI is beginning to be installed on heavy machinery as well; safety features that automatically detect and stop excavators or cranes when people or obstacles are nearby are emerging. By applying AI to safety management, many “I didn’t notice” or “careless mistakes” can be drastically reduced, bringing sites closer to zero-accident operations.

Schedule and Progress Management

In large-scale construction projects, schedule management is crucial, and AI is proving effective here as well. There are technologies that automatically grasp on-site progress. For example, AI analyzes images taken daily by fixed cameras or drones to judge the construction’s work accomplished (how far work has progressed). This allows AI to objectively calculate “X% complete compared to the plan” without relying on human visual estimations. AI trained on data from numerous past projects can also predict the risk of schedule delay early based on current progress and deployed resources. If AI indicates “at this rate, the final stage may be delayed by Y days,” measures can be taken in advance. Going further, AI is beginning to be used for schedule optimization—simulating construction steps and heavy equipment placement to propose the shortest-completion plan. Although this is still limited to some implementations, in the future it may become common to base construction plans on AI-created schedules. In any case, AI use in progress and schedule management supports lean planning and rapid decision-making, leading to shorter construction periods and cost reductions.

Material Management

AI is also useful for ordering and inventory management of materials used on site. Construction requires proper procurement and management of a wide variety of materials—from concrete, rebar, and bolts to fuel for heavy machinery. By introducing AI, you can predict required material quantities from past construction data and current progress, preventing shortages and overstock. For example, one AI system analyzes sensor and daily report data from the site and predicts “rebar will run out in 3 days,” prompting an early reorder. This reduces the risk that work will stall while waiting for materials. There are also cases where image-recognition AI automatically counts items in material yards or streamlines incoming inspection. Further, AI that learns material price fluctuation data to suggest optimal procurement timing is being researched. Using AI for material management not only supports smooth site progress but also contributes to cost compression by avoiding excess inventory.

Quality Inspection

AI is active in post-construction quality inspections and finish checks. Traditionally, cracks in concrete or uneven finishing surfaces depended on the eyes of experienced workers, but image-recognition AI now enables automatic inspection. For example, systems exist that photograph wall tiles or paint finishes and use AI to detect defects. Because AI can reveal minute defects that human eyes may miss with high accuracy, it helps correct quality issues early.

Major general contractors have also developed technology to automate rebar inspection by detecting the number and spacing of rebars with AI. This significantly shortens inspection time and improves inspection accuracy.

Advanced initiatives combining AI, 3D scanning, and AR (augmented reality) have also begun. When you scan a structure with a smartphone or tablet, AI identifies defect locations and marks them in the real world using AR. Inspectors can then fix the exact spots indicated on the screen, reducing missed rework. These AI-driven enhancements to quality inspection are expected to standardize and speed up inspection processes. In the future, AI will likely become an inspector’s constant partner, serving as the final bastion of quality assurance on site.

Introduction of AI: Case Studies

Here are some actual cases where AI has been used to achieve results.

• Survey efficiency through drone × AI (Obayashi Corporation): Obayashi Corporation introduced drones and AI analysis for civil engineering surveys. They built a system that automatically generates orthophotos and point cloud models in the cloud from photos captured by drones, greatly simplifying data processing that previously required dedicated technicians. As a result, survey work time was reduced to a fraction of what it used to be, enabling accurate site condition data to be obtained on the same day. Because data sharing is also smooth in the cloud, all stakeholders can grasp the latest terrain information in real time.

• Automation of rebar inspection using AI (Obayashi Corporation): Obayashi developed a system to automate rebar inspection in reinforced concrete structures using AI. Photos of rebar taken on site are analyzed by AI to automatically measure and judge counts, diameters, and spacing. The accuracy has reached a level comparable to experienced inspectors, successfully reducing inspection time significantly. Since AI judgment results are visualized, site supervisors can efficiently confirm corrections. Rebar inspections that used to take half a day can now be completed in a short time, reducing quality variability.

• AI cameras for safety monitoring (Taisei Corporation): Taisei Corporation prototyped a “smart helmet” that combines a 360-degree camera attached to a worker’s helmet with AI image analysis to manage site safety and monitor progress. The footage captured by the camera is analyzed by AI in real time to detect dangerous behaviors and automatically check work area accomplishment. If an anomaly is detected, a notification is immediately sent to managers, allowing remote site monitoring. This helps streamline safety patrols and reduce human error. They also plan to utilize accumulated site footage data for future early warning detection of work-related accidents.

Although the above are examples from major companies, small and medium-sized construction firms are also beginning to adopt cloud services and smart construction systems with AI. With support from the Ministry of Land, Infrastructure, Transport and Tourism, local construction sites are incorporating drone surveying and automated machine control, and there are increasing cases showing more than 20% productivity improvement. Across the construction industry, the wave of DX centered on AI is steadily spreading.

Cautions When Introducing AI

Introducing AI on site is not about blindly buying tools. To maximize effectiveness, pay attention to the following points:

• Clarify objectives and issues: First, it is important to clearly define which on-site problems you want to solve with AI. If you introduce technology just because it is the latest, it will not take root on site. Identify specific issues such as “surveying takes too long” or “human error causes frequent mistakes,” and select AI solutions suited to those problems.

• Form on-site consensus: It is also essential to gain understanding and cooperation from the people who will actually use the system on site. To reduce resistance to new systems, hold briefings and demos for site staff before introduction to share the benefits and how to use them. If you can customize while incorporating on-site voices, the workforce will engage proactively.

• Test on a small scale first: Rather than full-scale rollout across all sites at once, start with pilot sites or specific processes for trial operation. Conduct pilot projects to verify effectiveness and issues, and improve problems before full deployment to reduce the risk of failure.

• Prepare data and environment: Data is the lifeblood of AI. If you introduce image AI, establish shooting rules and ensure image quality; if using sensors, set up a communication environment—these preparations are keys to success. If training data such as past drawings and construction records is required, organize and digitize it in advance.

• Define operation rules and follow-up: You must also define operational workflows after the system is installed. For example, determine in advance who will respond and how when AI issues a warning, and who will check the data. Immediately after introduction, assign staff to answer on-site questions and quickly handle malfunctions; a follow-up system is indispensable. Make active use of vendor support as well.

• Consider legal regulations and privacy: As with drone flights that may require permission, using new technologies requires compliance with related legal regulations. Privacy considerations are also important for systems that constantly monitor with cameras. When introducing such systems, thoroughly examine legal compliance and ethical responses.

Technical and Human Hurdles

Be aware of the technical and human challenges you may face when advancing AI adoption.

• Cost barrier: AI introduction involves initial and running costs. Equipment purchases, software usage fees, and employee training can be burdensome for small and medium-sized companies. However, inexpensive solutions that can be tried due to cloud services are increasing, and national subsidy programs can be utilized. It is necessary to calculate cost-effectiveness and clarify long-term benefits and payback prospects.

• Lack of personnel and skills: Many site workers are not familiar with digital technologies, and the shortage of DX personnel who can handle AI is a challenge. There may be little time to devote to training. Consider initiatives to raise IT literacy within the company or receive support from external specialists. Also, to have AI learn the expertise of veteran workers, you need to devise ways to convert tacit knowledge into explicit knowledge.

• On-site environment and infrastructure: Construction sites often have unstable communication environments or difficulty ensuring power. If you use cloud AI, you need to improve the site’s communication environment, and if you handle large volumes of data, high-performance PCs or tablets are required. Preparing this infrastructure may require time and cost.

• Integration with existing systems: AI tools alone may not maximize effectiveness. Many cases become truly convenient only when integrated with existing construction management systems, accounting systems, or BIM/CIM data. System integration requires technical adjustments and customization, which can be a hurdle for companies with limited IT knowledge. Proceed with integration gradually while consulting vendors.

• Organizational culture change: Finally, there is the hurdle of changing human mindsets. The construction industry has long-standing practices and a craftsman culture, and there can be psychological resistance to new technologies. It takes time to dispel the pride of “we’ve always done it this way” and vague anxieties about AI. Management must lead DX promotion and foster a new culture together with the field. Create an environment that tolerates failure and praises challenge, and support DX from both top-down and bottom-up approaches.

Using Smartphone + GNSS for Simple Surveying: LRTK as the First Step in On-Site DX

Finally, we introduce LRTK as a solution highly compatible with AI and ideal as an entry point for on-site DX. LRTK (pronounced “L-R-T-K”) is a system that combines a smartphone with high-precision GNSS to enable centimeter-level positioning that anyone can easily perform—a simple surveying tool. Tasks that used to require specialized equipment and skilled technicians can be performed intuitively and with high accuracy using a smartphone with LRTK. For example, a current-condition survey that took several people half a day can be completed quickly by one person with LRTK, greatly contributing to time savings and workforce reduction.

Position and photo data acquired with LRTK can be directly used for AI-based point cloud analysis and AR display. If AI analyzes high-precision 3D survey data collected by a smartphone, as-built judgments and quantity calculations can be performed instantly. Also, because LRTK provides accurately linked position information, AR displays that overlay design models onto the real world on a smartphone or tablet can be performed with high precision. For example, at a construction site you can confirm the projected completion image or inspection points superimposed onto the real scenery through your phone screen, enabling intuitive and easy-to-understand site management.

While LRTK is excellent as a standalone survey DX tool, it is even more effective when combined with AI technologies, point cloud processing, and AR visualization. Initial costs are lower than traditional surveying equipment, and as an i-Construction-compliant product supported by the Ministry of Land, Infrastructure, Transport and Tourism, it offers peace of mind. As a first step in on-site DX, starting with digital surveying using LRTK allows you to smoothly connect the obtained data to AI analysis and other DX measures. LRTK can be a reliable partner in achieving smart construction management suited to the AI era.