PRACTICAL PREDICTION MODEL VERIFICATION AND REGRESSION ANALYSIS BY MACHINE LEARNING USING CONCRETE DRYING SHRINKAGE DATABASE

Abstract

In this study, the prediction and regression analysis of shrinkage during 6 months of drying period were carried out for the database of drying shrinkage experiments of concrete by using the JSCE prediction model and several machine learning models. In the machine learning, the default model parameter settings and the models with optimized parameters were compared. As a result, the parameter optimization slightly improved the prediction accuracy but the database and the training model used in this study were able to predict at a certain level of accuracy even with the default settings. In addition, accuracy of the machine learning prediction of shrinkage for 6 months of drying significantly was improved when the shrinkage strain for 28 days drying period was used as a predictor. In the importance analysis of the predictors, the importances of unit water content and aggregate density on the prediction of concrete shrinkage with 6 months drying were high. When 28-day dry shrinkage strain was included as a predictor, the importance was the highest while the importances of aggregate properties were relatively reduced. It was suggested that 28-day dry shrinkage strain partially includes the influence of materials properties and other parame-ters on 6 months drying shrinkage, which would be critical for drying shrinkage increase even not taken into accout in the design parameter.