Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Reza Maalek,Janaka Y Ruwanpura,Derek D Lichti

doi:10.3390/rs11091102

Reza Maalek, Janaka Y Ruwanpura + Show 1 more

Open Access

https://doi.org/10.3390/rs11091102

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Journal: Remote sensing	Publication Date: May 8, 2019
Citations: 82	License type: CC BY 4.0

Affiliation: University of Calgary

Abstract

This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

Highlights

In construction projects, as-designed vs. as-built dimensional incompliances result in rework, which can cost up to 25% of the contracted construction cost [1]
This study provides a robust solution to the semantic labeling of common reinforced concrete elements from point clouds using the spatial relationship, method of construction, and systematic thresholds, adopted from reliable standards of reinforced concrete construction
Five sets of terrestrial laser scanner (TLS) point clouds were acquired from the Graduate Student Hall of Residence (GSHR) construction site (Figure 6) at the University of Calgary

Summary

Introduction

As-designed vs. as-built dimensional incompliances result in rework, which can cost up to 25% of the contracted construction cost [1]. Rework has shown to be a dominant factor in increasing concrete waste with the highest impact on project cost over-run due to waste, compared to other construction materials [2]. Time, and possible environmental impact associated with rework in concrete construction, dimensional errors and structural damages may impact structural integrity, diminishing safety during and after construction. And accurate identification and reporting of delays, cost over-runs, rework, and structural instabilities through continuous inspection and as-built documentation are imperative to enabling project proponents to take corrective measures on time. An accurate and reliable as-built 3D/4D building information model (BIM) is beneficial during construction, and during facility operations for maintenance work [6] as well as sustainability and waste management [7]

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Conclusion