Towards Automatic Digital Documentation and Progress Reporting of Mechanical Construction Pipes using Smartphones

TL;DR

This paper introduces a smartphone-based framework for automated digital documentation and progress reporting of mechanical construction pipes, achieving high accuracy in pipe classification and measurement in real construction environments.

Contribution

The study presents novel methods for optimizing video frame rate, defining metric scale for 3D reconstruction, and classifying pipes from point clouds, validated in both lab and field conditions.

Findings

Achieved sub-millimeter pipe radius estimation accuracy.

Improved point cloud and classification quality with increased image overlap.

Attained 96.4% pipe classification F-measure and 5.4mm radius accuracy on-site.

Abstract

This manuscript presents a new framework towards automated digital documentation and progress reporting of mechanical pipes in building construction projects, using smartphones. New methods were proposed to optimize video frame rate to achieve a desired image overlap; define metric scale for 3D reconstruction; extract pipes from point clouds; and classify pipes according to their planned bill of quantity radii. The effectiveness of the proposed methods in both laboratory (six pipes) and construction site (58 pipes) conditions was evaluated. It was observed that the proposed metric scale definition achieved sub-millimeter pipe radius estimation accuracy. Both laboratory and field experiments revealed that increasing the defined image overlap improved point cloud quality, pipe classification quality, and pipe radius/length estimation. Overall, it was found possible to achieve pipe classification F-measure, radius estimation accuracy, and length estimation percent error of 96.4%, 5.4mm, and 5.0%, respectively, on construction sites using at least 95% image overlap.