Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using a novel regularized deep learning approach

Hoang Nhat-Duc1,2
1Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
2Faculty of Civil Engineering, Duy Tan University, Da Nang, Vietnam

Tóm tắt

The use of ground granulated blast furnace slag (GGBFS) helps reduce carbon dioxide generated during the manufacturing of ordinary Portland cement. The compressive strength (CS) is a crucial property of concrete mixtures, which is mandatorily required in the design and construction of concrete structures. This property of the GGBFS-blended concrete must be estimated accurately and reliably. This study proposes a novel deep neural network regression (DNNR) approach for generalizing the mapping relationship between the CS and its influencing variables. A data set consisting of 533 testing samples is used to train and verify the DNNR model. The contents of cement, GGBFS, water, plasticizer, coarse aggregate, and fine aggregate, as well as the concrete’s age are employed as predictor variables. To alleviate the overfitting issue, L1, L2, and weight decay regularizations are used during the training phase of the DNNR model. Moreover, to reduce of the number of cases in which the CS values are overestimated, this study relies on the utilization of an asymmetric loss function to construct the deep learning-based method. Experimental results point out that the regularized DNNR can help attain an outstanding prediction performance with a root mean square error of 2.78, a mean absolute percentage error of 5.7%, and a coefficient of determination of 0.98. In addition, the use of the asymmetric loss function can help reduce the percentage of overestimated cases from roughly 50–36%. Therefore, the proposed deep learning model can be a potential alternative for predicting the CS of the GGBFS-blended concrete mixtures.

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