Estimating the Workability of Concrete with a Stereovision Camera during Mixing
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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Author
Date
2024-07
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
23
Series
Sensors, Volume 24, issue 14
Abstract
The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this paper, we developed a machine-learning framework for estimating the slump class of concrete in the mixer using a stereovision camera. Depth data from five different slump classes was transformed into Haralick texture features to train several machine-learning classifiers. The best-performing classifier achieved a multiclass classification accuracy of 0.8179 with the XGBoost algorithm. Furthermore, we found through statistical analysis that while the denoising of depth data has little effect on the accuracy, the feature extraction of mixer blades and the choice of region of interest significantly increase the accuracy and the efficiency of the classifiers. The proposed framework shows robust results, indicating that stereovision is a competitive solution to estimate the workability of concrete during concrete production.Description
Publisher Copyright: © 2024 by the authors.
Keywords
computer vision, concrete, machine learning, stereovision, workability
Other note
Citation
Ojala, T & Punkki, J 2024, ' Estimating the Workability of Concrete with a Stereovision Camera during Mixing ', Sensors, vol. 24, no. 14, 4472 . https://doi.org/10.3390/s24144472
