In modern infrastructure construction, quality control of reinforced concrete structures remains one of the most labour-intensive processes. Checking reinforcement cages and creating as-built documentation traditionally require significant time and human resources: field measurements, photo documentation, data processing, and report preparation.
Our Swiss partner Pix4D, which specialises in mapping and photogrammetry software, shares a practical case study where reinforcement inspection is carried out using a smartphone and the Pix4Dcatch solution. It was integrated with the Modely system, a specialised platform for 3D reinforcement inspection. This approach made it possible to transition from traditional photo-based and manual measurements to a digital model based on point clouds.
The case study was implemented as part of the Kansai Main Line modernisation project, a key railway line connecting Osaka and Nagoya.
Project challenges
Many of the tested smartphone-based 3D measurement solutions did not meet professional requirements: gaps appeared in the point cloud, reinforcement was reproduced inaccurately, and the results depended on the operator. In contrast, Pix4Dcatch demonstrated stable results thanks to its use of hybrid photogrammetry and LiDAR technology, which allowed even thin reinforcement bars to be accurately captured without data loss.
An important advantage was the ability to perform measurements without using ground control points (GCPs). On active construction sites, where reinforcement frames are assembled at a rapid pace, setting markers is often a critical limitation.
With Pix4Dcatch, the operator simply walks around the structure with a smartphone to quickly create a dense point cloud without interrupting work processes on site.
Point cloud data captured with PIX4Dcatch – clarity made possible by the hybrid of photogrammetry and smartphone LiDAR
Project stages
The implemented workflow consisted of several stages:
1. Capture. The operator scanned the reinforcement cage using PIX4Dcatch on an iPhone Pro.
2. Processing. A point cloud is automatically generated in PIX4Dcloud.
3. Model cleaning. Extra points and noise are removed from the data.
4. Analysis. The model is imported into the Modely system, which performs automated reinforcement recognition, geometry verification, and report generation.
This approach allows a quick transition from field data to analytics and structural quality control.
Detection of rebar hoops
Detection of main rebars in the rebar cage
Project results
Conceptual visualization of the railway viaduct foundation, featuring the 129 cast-in-place piles managed during the project
Number of photos taken:
- before implementation: 81 photos/frame × 129 frames (total: 10,449 photos)
- after implementation: 39 photos/frame × 129 frames (total: 5,031 photos)
Time savings:
- 56% reduction in inspection time per frame
- total savings of approximately 850 working hours (106 man-days)
As a result, the reduction in manual operations—including photographing, measuring, sorting images, and preparing reports—significantly reduced the workload on engineering staff.
Conclusion
The implementation of 3D reinforcement inspection using Pix4Dcatch has shown that mobile photogrammetric tools can be an effective element of digital transformation in the construction industry.
The combination of Pix4Dcatch and Modely has created a workflow that provides:
- consistent measurement accuracy
- reproducibility of results
- fast data processing without complex equipment
This approach is particularly effective for structures with a standardised reinforcement scheme, such as bored piles, and has the potential to be scaled up to more complex infrastructure projects.
