Shrinkage and Crack Data and Program

In this study, the concrete shrinkage and creep laboraty data is analyzed based on the regression by ma- chine learning, linear regression and the design empirical predictionin Japan. The random forect predicted the ultimate shrinkage under various conditions most accurately, while the ultimate creep was estimiated by the neural network with maximum accuracy. It was found that the machine learning can approximately predict shrinkage and creep under conditions beyond the design range but is not able to estimate them under extreme conditions such very high relative humidiy close to 100 %, high water-to-cement ratio over 0.8 and others The importance of parameters according to the randam forest was reasonable to reflect shrinkage and creep characteristics known by laboratory test and design. The machine learning based on the laboratory experiment cannot reasonably predict the variation of shrinkage and creep whose learning data is few and the extrapolating long-term behavior.