Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Abstract

Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

“…Existing studies indicate that models based on machine learning and deep learning can predict the compressive strength of concrete with relatively high accuracy [30][31][32]. For instance, predictive models constructed using BP neural networks have shown good predictive performance regarding the compressive strength of different types of concrete [33][34][35][36][37]. Additionally, other machine learning methods, besides BP neural networks, have demonstrated a favorable trend in predicting the 28-day compressive strength of concrete [38][39][40][41][42].…”