Prediction of reinforced fly ash concrete columns’ behavior under eccentric loads using Gaussian process regression
Abstract
Fly ash has been increasingly utilized in concrete industry owing to its advantages in improving workability of fresh concrete and some properties of harden concrete, reducing material cost and adapting sustainable construction requirements. This article introduces a Gaussian process regression using machine learning approach to predict the behavior of reinforced fly ash concrete (RFAC) columns in the form of axial load (N) - mid-height lateral displacement (∆) relation curve. A dataset collected from an experimental study conducted by the authors and checked to be in accordance with TCVN 5574:2018, is used to train the model with the ratio of 10%. Once being well validated by the remaining 90% of the dataset, it is shown that the model is capable of predicting the N-∆ curves so that the behavior of the tested RFAC columns when subjected to various levels of load eccentricity can be observed and the ultimate resistance of the columns under such condition can be determined.
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