Prediction of expansion of electric arc furnace oxidizing slag mortar using MNLR and BPN

The present study established prediction models based on multiple nonlinear regressions (MNLRs) and back-propagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either 80 degrees C or 100 degrees C for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited R-2 values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.