This research studied the effect of recycled steel fibers extracted from twisted cable waste on the fresh and hardened states of high-performance concretes. Accordingly, slump, water absorption (WA), compressive strength (CS), flexural strength (FS), and split tensile strength (STS) were measured in the laboratory using mixtures generated by the response surface methodology (RSM). The RSM-based central composite design (CCD) was used to assess the influence of water-to-binder (W/B) ratios from 0.27 to 0.31, length-to-diameter (L/d = 46 to 80) and steel fiber content (SFC) in the range of 19 to 29 kg/m³ on the behavior of high-performance fiber-reinforced concrete (HPFRC). The accuracy and validation of the generated models were evaluated by employing analysis of variance (ANOVA) and optimal parameters. The experimental findings revealed that the use of an L/d ratio of 63, a W/B ratio of approximately 0.28, and an SFC of around 22 kg/m³ resulted in high workability in terms of slump. While a notable increase in compressive strength was observed when employing an L/d ratio of approximately 70, a W/B ratio of around 0.28, and the maximum SFC of 29 kg/m³, as confirmed by scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis.

This study aims to develop an Artificial Neural Network (ANN) model to predict the Plasticity Index (PI) of clay soils stabilized with fly ash. A total of 193 datasets were collected from previous studies, normalized, and used for training, testing, and validating the model using the backpropagation algorithm. The results indicated that the PI-ANN model achieved high accuracy, with a correlation coefficient exceeding 99% and minimal Mean Square Error (MSE). Sensitivity analysis showed consistency with existing literature. Validation using experimental results from three independent studies confirmed the model’s performance, achieving a regression coefficient of 99% and a mean relative error of 0.92%. Additionally, the PI-ANN model outperformed a comparable statistical model, demonstrating its reliability for predicting the PI of high-plasticity clay soils.