A Data-Driven Approach Based on Deep Neural Network Regression for Predicting the Compressive Strength of Steel Fiber Reinforced Concrete

Estimating the compressive strength of steel fiber reinforced concrete (SFRC) is a crucial task required in mix design. Thus, a reliable method that can deliver accurate estimations of the compressive strength of SFRC is a practical need. This study puts forward a new deep neural network-based regression model for solving the task at hand. The state-of-the-art Nesterov accelerated adaptive moment estimation (Nadam) is used to optimize the deep neural computing model that learns the functional mapping between the compressive strength and concrete’s constituents. A dataset, consisting of 303 samples and 12 predictor variables, is used to train the deep learning approach. Notably, the current work has carried out a comparative study to identify the suitable regularization strategy for establishing a robust SFRC strength estimation model. Experimental results show that the L1 regularization helps achieve the most desired performance, with a coefficient of determination (R2) of roughly 0.96. Notably, an asymmetric loss function is used along with Nadam to decrease the percentage of overestimated cases from 50.83% to 27.08%. In general, the proposed method can be a promising tool to support construction engineers in SFRC mix design.