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http://dx.doi.org/10.9712/KASS.2020.20.4.111

Machine Learning Based Strength Prediction of UHPC for Spatial Structures  

Lee, Seunghye (Dept. of Architectural Engineering, Sejong Univ.)
Lee, Jaehong (Dept. of Architectural Engineering, Sejong Univ.)
Publication Information
Journal of Korean Association for Spatial Structures / v.20, no.4, 2020 , pp. 111-121 More about this Journal
Abstract
There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.
Keywords
UHPC; Spatial structure; Machine learning; Strength prediction; Deep learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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