Automatic as-built management of retaining grid frame structures using point cloud data

• End-to-end automated as-built management of retaining grid frame structures. • Segmentation, cross-sectioning and dimension extraction performed on point clouds. • Field test on a 60 m curved slope achieved 0.02 m mean dimensional error. • Method reached F1 = 0.92 for slope surface detection on TLS and HLS datasets. • Replaces hazardous rope-work with safe remote point-cloud inspection. Extreme weather and earthquakes are boosting demand for slope protection on steep terrain. Retaining grid frame structures—lattice-type concrete grids—must be surveyed precisely, yet rope-work-based manual measurements are labour-intensive and hazardous. This paper presents an as-built management method that (1) separates grid frame and slope surfaces in point cloud data, (2) creates cross-sections, and (3) outputs width, height and frame center distance for every frame. Tests on a 60 m curved site in Fukushima showed mean dimensional error of 0.02 m, satisfying the Japanese guideline tolerances (±0.10 m for frame center distance and ± 0.03 m for both width and height), and an F1-score of 0.92 for slope surface detection with reliable rope removal. The workflow lowers field labour, enhances safety.

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Publication Details

Journal
Measurement
Published
2026-03-03
DOI
https://doi.org/10.1016/j.measurement.2026.121018
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
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article

Automatic as-built management of retaining grid frame structures using point cloud data

Satoshi Fujita, Y. Niina, Ryuichi IMAI, Tarou Yamanashi et al.
Measurement
Remote Sensing and LiDAR Applications
article

Automatic as-built management of retaining grid frame structures using point cloud data

Satoshi Fujita, Y. Niina, Ryuichi IMAI, Tarou Yamanashi, Yoshinori TSUKADA, Kenji Nakamura, Yoshimasa Umehara, Yutaka Matsubayashi
article en

Abstract

• End-to-end automated as-built management of retaining grid frame structures. • Segmentation, cross-sectioning and dimension extraction performed on point clouds. • Field test on a 60 m curved slope achieved 0.02 m mean dimensional error. • Method reached F1 = 0.92 for slope surface detection on TLS and HLS datasets. • Replaces hazardous rope-work with safe remote point-cloud inspection. Extreme weather and earthquakes are boosting demand for slope protection on steep terrain. Retaining grid frame structures—lattice-type concrete grids—must be surveyed precisely, yet rope-work-based manual measurements are labour-intensive and hazardous. This paper presents an as-built management method that (1) separates grid frame and slope surfaces in point cloud data, (2) creates cross-sections, and (3) outputs width, height and frame center distance for every frame. Tests on a 60 m curved site in Fukushima showed mean dimensional error of 0.02 m, satisfying the Japanese guideline tolerances (±0.10 m for frame center distance and ± 0.03 m for both width and height), and an F1-score of 0.92 for slope surface detection with reliable rope removal. The workflow lowers field labour, enhances safety.

MeasurementVol. 272
Setsunan University (JP), Osaka University of Economics (JP), Reitaku University (JP), Hosei University (JP), Nitto Chemical Industry (Japan) (JP), Asiaq Greenland Survey (GL)
Openalex Percentile: Top 46%
Remote Sensing and LiDAR Applications
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