The rapid development in building construction has stimulated the replacement of cement in concrete with construction waste materials such as marble waste powder (MWP) and granite waste powder (GWP) to reduce the negative impact of cement production and to save natural resources. Therefore, the inclusion of these materials in concrete contributes to environmental sustainability by reducing cement consumption and promoting the reuse of industrial waste. The present study employs Response Surface Methodology (RSM) and, for the first time in a comparable context, the Neural Network Algorithm (NNA) as an advanced optimization and predictive tool to evaluate the synergistic effect of using GWP and MWP as partial cement replacements in concrete exposed to elevated temperatures. The study involved four independent variables: replacement level of GWP up to 9%, replacement level of MWP up to 9%, the degree of temperature (T) up to 800 °C, and the exposure duration (D) up to 2 h, while the dependent variable (output) was the compressive strength (CS). The ANOVA results revealed that the quadratic model outperformed the linear model in predicting the CS of concrete. The Quadratic model, derived from RSM, demonstrated superior performance in predicting CS values. However, the NNA model also showed high predictive accuracy (R² = 0.949; RMSE = 1.5297 MPa), effectively capturing the complex and nonlinear relationships among temperature, duration, and the cement replacement levels with GWP and MWP. The optimization results revealed that the maximum compressive strength of 39.4 MPa can be achieved at 8.92% GWP, 1.89% MWP, T of 247 °C, and D of 0.64 h with a desirability of 1. The proposed models provided valuable insights into the synergistic effects of granite and marble waste powders, supporting the design of sustainable, high-performance concrete with reduced environmental footprint and improved resource efficiency.

This study utilizes response surface methodology (RSM) to examine the effectiveness of several nanomaterials on the residual compressive strength (CS) of concrete exposed to high temperature. Three nanoparticles were used as partial cement replacement by weight at replacement ratios up to 5%: nano-granodiorite (NG), nano-magnetite (NM), and nano-lead oxide (NL). The specimens were exposed to temperatures of 400 °C, 600 °C, and 800 °C, and the residual compressive strength was determined. The experimental results exhibited that all nanoparticles in concrete specimens reduced the loss of strength at high temperature. The control specimen lost 48.39% of its compressive strength at 800 °C. A dose of 4% NG and NM resulted in an increase in residual strength of 37.87% and 22.06%, respectively, while 3% NL specimens resulted in 11.4% improvement. A regression model for predicting concrete CS was developed using RSM. The model was successful in capturing the high correlation between the input variables (temperature, type of nanomaterial, and nanoparticle dose) and the output of CS. A good agreement between model predictions and the experimental data was obtained. The results of CS optimization showed that 5% NG at 126 °C resulted in the highest compressive strength of 61.80 MPa, confirming its superiority over NM and NL for the formulation of concrete under thermal conditions. The results reveal that the utilized nanoparticles, especially NG, have great potential to be used as cementitious materials to improve the high-temperature resilience of concrete.