K-fold Validation Neural Network Approach for Predicting the One-Day Compressive Strength of UHPC

Abstract

Abstract This paper analyzes the application of artificial neural networks (ANN) to predict the 1-day compressive strength of ultra-high-performance concrete (UHPC) made with any combination of powders and supplementary cementitious materials (SCM) such as silica fume (SF), fly ash (FA), ground granulated blast slag furnace (GGBSF), recycled glass powder (GP), rice husk ash (RHA), fluid catalytic cracking catalyst residue (FC3R), metakaolin (MK), limestone powder (LP), and quartz powder (QP). A total of 604 data from scientific literature were used to train the one hidden layer ANN model by using the k-fold validation procedure. Furthermore, 90 UHPC mixtures were experimentally performed to validate the proposed ANN model. The performance of the model was assessed using several statistical performance indexes: ratio of the root mean square error to the standard deviation of measured data (RSR), root mean square error (RSME), normalized mean bias error (NMBE), Nash–Sutcliff efficiency, and coefficient of multiple determination (R2). Connection weight approach (CWA) algorithm was utilized to analyze the relationships between the UHPC components and the 1-day compressive strength. The results indicated that the ANN is an efficient model for predicting the early strength (1-day compressive strength) of UHPC achieving R2 values of 0.88 and 0.86 on the test data and experimental data, respectively, even when the experimental dosages included combinations of components that were not found in the training data. The results of the CWA analysis indicated that SCM such as MK, FC3R, SF, and LP, as well as other factors such as virtual packing density, improved the early strength of UHPC, whereas FA, GP, and RHA were pointed out as harmful for the one-day compressive strength. As conclusion, the ANN model could be helpful in the developing of UHPC with early strength needs by preselecting the combinations of available SCM and powders that have better results in the model at lower cost.