Development of a New Stacking Model to Evaluate the Strength Parameters of Concrete Samples in Laboratory

In this research, a new idea was implemented to combine different models of artificial intelligence (AI) for evaluating the strength parameters of concrete samples in laboratory. To do this, a series of laboratory data related to concrete samples containing eight different parameters were collected. The system was developed based on various AI models including multi-layer perceptron (MLP), random forest (RF), the k-nearest neighbors (KNN), and tree. The final model developed in this research was designed as a stacking-tree-RF-KNN-MLP structure. This new structure includes various characteristics of four different models that were optimized to achieve a stable structure to evaluate the compressive strength of concrete samples. Each of the basic models involves different parameters that affect the final system performance. By investigating and optimizing each of them, the stacking-tree-RF-KNN-MLP system was updated and finally the final model was obtained. This model covers the weaknesses of each of the basic AI models and uses their best performance in order to get higher prediction capacity. The results of coefficient of determination ( R 2 ) showed that the developed model has an accuracy of 0.995 and 0.962 for training and testing data, respectively, which is able to create a suitable structure to predict the compressive strength of concrete samples. In addition, the stacking-tree-RF-KNN-MLP model developed in this research, compared to other models, receives a lower level of system error. The newly developed model can be used in other fields related to construction and material for solving relevant problems.