How does automated construction progress monitoring work?
April 8, 2025
Automated construction progress monitoring uses AI-supported systems to monitor construction progress in real time. Camera systems are used almost exclusively for this. These capture data from the construction site, which is analyzed by algorithms to track progress and detect deviations.
The use of AI technologies varies depending on the construction phase, be it earthworks, shell construction or interior finishing. Each of these phases has specific requirements and uses different technical approaches to optimize processes and increase efficiency.
Drone use in civil engineering and earthworks
Technology description
Drones equipped with high-resolution cameras and LiDAR scanners capture detailed data from the construction site. This data is analyzed by AI algorithms to create topographical maps, monitor progress and identify potential problems at an early stage.
Concrete application:
Surface identification and fault detection: The drones capture 3D point clouds, which the AI uses to create surface models. These models help to monitor construction progress and detect irregularities or faults such as subsidence or erosion problems.
Volume calculations: AI analyzes the data to calculate the volume of pits and other structures and determine the change from a previous point in time.
Carcass framing with crane cameras
Technology description:
Cameras attached to cranes continuously capture video footage of the construction site. AI algorithms analyse these videos to track progress, document and optimize work.
Concrete application:
Process monitoring and documentation: The AI recognizes and documents human work and components. For example, it can recognize how many workers have been working on a wall, concreting a ceiling or a precast element on which floor and construction phase. Construction progress can be automatically documented by automatically assigning processes to a given schedule.
Optimization (pack down if necessary): By recording all work in detail, important key production figures such as trade utilization, throughput times or expense values can be collected. If a repetitive sequence of trades is analyzed in detail, acceleration potentials can be identified. A common example is the identification of time-critical components and the monitoring of capacity utilization at this point. The shell construction time can be reduced by 10 to 20% by adapting the cycle time to the real data.
Portable LiDAR scanners and 360° cameras capture detailed data of interior spaces. This data is processed by AI algorithms to create accurate 3D models and monitor the progress and quality of the work.
Concrete application:
3D modeling and quality control: The AI creates precise models of the interiors from the scanned 3D point clouds. These models help to track progress and check the execution of the work. Errors such as uneven surfaces or incorrect installations can be detected at an early stage.
Virtual inspections: Captured data enables virtual inspections, allowing site managers to remotely check progress and ensure work is meeting requirements.
Conclusion
Automated construction progress monitoring is revolutionizing the construction industry by enabling precise, efficient and continuous monitoring through the use of AI-supported camera systems and drones. The technology helps to make construction progress transparent and traceable, identify bottlenecks at an early stage and exploit optimization potential. It therefore makes a decisive contribution to the digitalization of construction processes and offers construction companies a clear competitive advantage.
