Evaluation of Machine Learning and Traditional Methods for Estimating Compressive Strength of UHPC

Abstract

This research provides a comparative analysis of the optimization of ultra-high-performance concrete (UHPC) using artificial neural network (ANN) and response surface methodology (RSM). By using ANN and RSM, the yield of UHPC was modeled and optimized as a function of 22 independent variables, including cement content, cement compressive strength, cement type, cement strength class, fly-ash, slag, silica-fume, nano-silica, limestone powder, sand, coarse aggregates, maximum aggregate size, quartz powder, water, super-plasticizers, polystyrene fiber, polystyrene fiber diameter, polystyrene fiber length, steel fiber content, steel fiber diameter, steel fiber length, and curing time. Two statistical parameters were examined based on their modeling, i.e., determination coefficient (R2) and mean square error (MSE). ANN and RSM were evaluated for their predictive and generalization capabilities using a different dataset from previously published research. Results show that RSM is computationally efficient and easy to interpret, whereas ANN is more accurate at predicting UHPC characteristics due to its nonlinear interactions. Results show that the ANN model (R = 0.95 and R2 = 0.91) and RSM model (R = 0.94, and R2 = 0.90) can predict UHPC compressive strength. The prediction error for optimal yield using an ANN and RSM was 3.5% and 7%, respectively. According to the ANN model’s sensitivity analysis, cement and water have a significant impact on compressive strength.