An optimized clustering approach to investigate the main features in predicting the punching shear capacity of steel fiber-reinforced concrete

Abstract

We developed an optimized system for solving engineering problems according to the characteristics of data. Because data analysis includes different variations, the use of common features can increase the performance and accuracy of models. Therefore, this study, using a combination of optimization techniques (K-means algorithm) and prediction techniques, offers a new system and procedure that can identify and analyze data with similarity and close grouping. The system developed using the new sparrow search algorithm (SSA) has been updated as a new hybrid solution to optimize development engineering problems. The data for proposing the mentioned techniques were collected from a series of laboratory works on samples of steel fiber-reinforced concrete (SFRC). To investigate the issue, the data were first divided into different clusters, taking into account common features. After introducing the top clusters, each cluster was developed using three predictive models, i.e., multi-layer perceptron (MLP), support vector regression (SVR), and tree-based techniques. This process continues until the criteria are met. Accordingly, the K-means–artificial neural network 3 structure shows the best performance in terms of accuracy and error. The results also showed that the structure of hybrid models with cluster numbers 2, 3, and 4 is higher than the baseline models in terms of accuracy for assessing the punching shear capacity (PSC) of SFRC. The K-means–ANN3-SSA generated a new methodology for optimizing PSC. The new proposed model/procedure can be used for a similar situation by combining clustering and prediction methods.