Deakin University
Browse

File(s) under permanent embargo

Imaging network design to improve the automated construction progress monitoring process

Version 2 2024-06-04, 12:09
Version 1 2019-05-16, 13:49
journal contribution
posted on 2024-06-04, 12:09 authored by H Mahami, Farnad NasirzadehFarnad Nasirzadeh, A Hosseininaveh Ahmadabadian, F Esmaeili, S Nahavandi
Purpose This paper aims to propose an automatic imaging network design to improve the efficiency and accuracy of automated construction progress monitoring. The proposed method will address two shortcomings of the previous studies, including the large number of captured images required and the incompleteness and inaccuracy of generated as-built models. Design/methodology/approach Using the proposed method, the number of required images is minimized in two stages. In the first stage, the manual photogrammetric network design is used to decrease the number of camera stations considering proper constraints. Then the image acquisition is done and the captured images are used to generate 3D points cloud model. In the second stage, a new software for automatic imaging network design is developed and used to cluster and select the optimal images automatically, using the existing dense points cloud model generated before, and the final optimum camera stations are determined. Therefore, the automated progress monitoring can be done by imaging at the selected camera stations to produce periodic progress reports. Findings The achieved results show that using the proposed manual and automatic imaging network design methods, the number of required images is decreased by 65 and 75 per cent, respectively. Moreover, the accuracy and completeness of points cloud reconstruction is improved and the quantity of performed work is determined with the accuracy, which is close to 100 per cent. Practical implications It is believed that the proposed method may present a novel and robust tool for automated progress monitoring using unmanned aerial vehicles and based on photogrammetry and computer vision techniques. Using the proposed method, the number of required images is minimized, and the accuracy and completeness of points cloud reconstruction is improved. Originality/value To generate the points cloud reconstruction based on close-range photogrammetry principles, more than hundreds of images must be captured and processed, which is time-consuming and labor-intensive. There has been no previous study to reduce the large number of required captured images. Moreover, lack of images in some areas leads to an incomplete or inaccurate model. This research resolves the mentioned shortcomings.

History

Journal

Construction Innovation

Volume

19

Pagination

386-404

Location

Bingley, Eng.

ISSN

1471-4175

eISSN

1477-0857

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Emerald Publishing Limited

Issue

3

Publisher

EMERALD GROUP PUBLISHING LTD