Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

Concrete and cement have been widely used in past decades as a result of urbanization. More and more supplementary cementitious materials are adopted in concrete because its production complements environmental conservation. The influence of slag, fly ash, limestone, etc., on compressive strength of concrete is of interest to engineers worldwide. Many previous studies were specific to certain engineering or certain experiments that could not reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength. The research concerning the influence of two or more kinds of supplementary cementitious materials on concrete’s compressive strength is still unclear. Moreover, there is a lack of clarity on the optimum proportion of one or more certain cementitious materials in practical engineering or experiments. To overcome these problems, this study adopts the concrete compressive strength development over time (CCSDOT) model, which generates an explicit formula to conduct quantitative research based on extensive data. The CCSDOT model performs well in fitting the compressive strength development of concrete containing cement, slag, fly ash, and limestone flour. The results reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength through the parameter analysis in the model. Two application cases are analyzed concerning the selection of the three supplementary cementitious materials and design of concrete mix proportion for practical engineering. It is concluded that the CCSDOT model and the method in this study can possibly provide guidance on both the selection of supplementary cementitious materials and the design of optimal concrete mix proportion for practical engineering. Therefore, the study is highly essential and useful.