Prediction of concrete strength using response surface function modified depth neural network

Abstract

In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper. The loss function based on the data on the response surface was derived, DNN based on multivariable response surface function (MRSF-DNN) was established. MRSF-DNN model of recycled brick aggregate concrete compressive strength was established, in which coarse aggregate volume content, fine aggregate volume content and water cement ratio are influencing factors. Furthermore, the predictive analysis and extended analysis of MRSF-DNN model were carried out. The results show that: MRSF-DNN model had high prediction accuracy, the correlation coefficient between the real values and the forecast values was 0.9882, the relative error was between -0.5% and 1%. Furthermore, MRSF-DNN had more stable prediction ability and stronger generalization ability than DNN.

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

“…Artificial neural networks are capable of capturing hidden nonlinear rela-tionships in data, which is an important property when trying to predict the concrete's strength [33][34][35]. Deep neural networks also have high prediction accuracy; the correlation coefficient between real and predicted values when used in [36] is 0.9882, and the relative error was up to 1%. Hybrid models are a combination of different approaches and methods for solving problems [37,38], combining the advantages of different models and algorithms to achieve a more efficient and accurate solution to the problem.…”