This study focused on the evaluation of the mechanical and durability aspects of stabilized poorly graded sandy soil by activated blast furnace slag cements (ASCs) with three different activators with combinations of ${\mathrm{Na}}_2{\mathrm{SiO}}_3$ and NaOH (PN), ${\mathrm{Na}}_2{\mathrm{CO}}_3$ and $\mathrm{Ca}(\mathrm{OH}{)}_2$ (PC), and ${\mathrm{Na}}_2{\mathrm{SO}}_4$ and $\mathrm{Ca}(\mathrm{OH}{)}_2$ (PS). Type of activators, binder percentage, and curing time were investigated as parameters. The results of unconfined compression strength (UCS) of samples in a constant amount of binder/soil (5-wt%) showed an increase from 0.75-MPa for ordinary portland cement (OPC)-based samples to 2.28, 2.63, and 4.5-MPa for PC-, PN-, and PS-based samples, respectively. Due to its high UCS sample value, the activator considered most effective is PS based. The formed gel between the soil’s particles results from the dense microstructure and fewer voids in the sample suggests a higher interaction level between soil and binder. PS- and PC-based binders showed more ductility than OPC-based stabilized soil. Moreover, ASC samples showed better soundness than OPC samples, due to less volume changes, stronger bonds between soil particles, binder matrix, and absence of destructive expansion.

This paper aims to investigate the effect of fine recycled concrete powder (FRCP) on the strength of self-compacting concrete (SCC). For this purpose, a numerical artificial neural network (ANN) model was developed for strength prediction of SCC incorporating FRCP. At first, 240 experimental data sets were selected from the literature to develop the model. Approximately 60% of the database was used for training, 20% for testing, and the remaining 20% for the validation step. Model inputs included binder content, water/binder ratio, recycled concrete aggregates’ (RCA) content, percentage of supplementary cementitious materials (fly ash), amount of FRCP, and curing time. The model provided reliable results with mean square error (MSE) and regression values of 0.01 and 0.97, respectively. Additionally, to further validate the model, four experimental recycled self-compacting concrete (RSCC) samples were tested experimentally, and their properties were used as unseen data to the model. The results showed that the developed model can predict the compressive strength of RSCC with high accuracy.