Fresh and Hardened Properties of Cementitious Composites Incorporating Firebrick Powder from Construction and Demolition Waste

Firebricks are generally used in furnace basins where glass, ceramics, and cement are produced. Firebricks have an important place in construction and demolition waste (CDW). However, there is a limited understanding of the effects on fresh and hardened state properties of cementitious composites. This study investigates the mechanical, physical, and microstructural properties of cementitious composites incorporating firebrick powder (FBP) from CDW. In this regard, the FBP was used at 5, 10, 15, 20, and 25% replacement ratio by weight of cement to produce cementitious composites. The consistency, setting characteristics, and 3, 7, and 28 days compressive and flexural strength tests of produced cementitious composites were performed. In addition, ultrasonic pulse velocity, water absorption, porosity, unit weight, and microstructure analysis of cementitious composites were conducted. As a result, the 28-day compressive strength of the cementitious composite mortars containing up to 10% firebrick powder remained above 42.5 MPa. The flow diameters increased significantly with the increase of the FBP. Therefore, it has been determined that the FBP can be used up to 10% in cementitious composites that require load-bearing properties. However, FBP might be used up to 25% in some cases. Using waste FBP instead of cement would reduce the amount of cement used and lower the cost of producing cementitious composites

Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

This study has two main objectives: (i) to investigate the parameters affecting the compressive strength (CS) of perlite-containing slag-based geopolymers and (ii) to predict the CS values obtained from experimental studies. In this regard, 540 cubic geopolymer samples incorporating different raw perlite powder (RPP) replacement ratios, different sodium hydroxide (NaOH) molarity, different curing time, and different curing temperatures for a total of 180 mixture groups were produced and their CS results were experimentally determined. Then conventional regression analysis (CRA), multivariate adaptive regression splines (MARS), and TreeNet methods, as well as artificial neural network (ANN) methods, were used to predict the CS results of geopolymers using this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Nash-Sutcliffe (NS) performance statistics were used to evaluate the CS prediction capabilities of the methods. As a result, it was determined that the optimum molarity, curing time, and curing temperature were 14 M, 24 h, and 110 celcius, respectively and 48 h of heat curing did not have a significant effect on increasing the CS of the geopolymers. The highest performances in regression-based models were obtained from the MARS method. However, the ANN method showed higher prediction performance than the regression-based methods. Considering the RMSE values, it was seen that the ANN method made improvements by 24.7, 2.1, and 13.7 %, respectively, compared to the MARS method for training, validation, and test sets