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.

在本研究中，基于机器学习、线性回归以及日本的设计经验预测，对混凝土收缩与蠕变实验室数据进行了分析。随机森林模型在预测各种条件下的最终收缩值方面表现出最高的准确性，而神经网络模型在估算最终蠕变值方面实现了最大精度。研究发现，机器学习能够在设计范围之外的条件近似预测收缩与蠕变，但在极端条件下，如接近100%的高相对湿度、水灰比超过0.8等情况，则无法进行估算。根据随机森林模型的重要性参数，能够合理地反映实验室测试和设计中已知的收缩与蠕变特性。基于实验室实验的机器学习无法合理预测收缩与蠕变的变异，尤其是当学习数据较少时，以及外推长期行为时。