Abstract
In this study, we surveyed ready-mixed concrete shipped in Okinawa Prefecture from June 2020 to December 2021 to investigate the actual condition of concrete at the time of mixing and unloading using concrete formulation, material temperature, environmental and transportation information. In addition, Random Forest and LightGBM were used to learn to predict the concrete temperature at the time of mixing and unloading from each factor in the collected data. In addition, the effect of features on the prediction accuracy of the learning model was evaluated by Partial Dependence Plot.
Translated title of the contribution | YEAR-ROUND SURVEY OF READY-MIXED CONCRETE TEMPERATURE IN OKINAWA PREFECTURE AND CONCRETE TEMPERATURE ESTIMATION USING MACHINE LEARNING |
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Original language | Japanese |
Pages (from-to) | 633-638 |
Number of pages | 6 |
Journal | AIJ Journal of Technology and Design |
Volume | 29 |
Issue number | 72 |
DOIs | |
Publication status | Published - Jun 1 2023 |
All Science Journal Classification (ASJC) codes
- Architecture
- Building and Construction