Synergistic Effects of Brick Waste and Fly Ash on Concrete Performance: An Experimental and Computational Study

Synergistic Effects of

Sustainable concrete incorporating bagasse ash and plastic waste as fine aggregate

The construction industry is increasingly adopting sustainable materials to reduce environmental impacts while maintaining structural performance. This study investigates the combined use of sugarcane bagasse ash (BA) and polyethylene terephthalate (PET) aggregates in M30-grade concrete. BA replaced cement at 0%–15%, while PET replaced natural sand at 0%–20%. The control mix achieved a compressive strength of 37.25 MPa, while the 5%BA + 10%PET mix attained 36.46 MPa (≈2% reduction). At 20% PET replacement, compressive strength decreased to 33.81 MPa (≈9% reduction). Similar trends were observed for split tensile strength (3.45–3.27 MPa) and flexural strength (3.79–3.62 MPa). The optimum mix (BA10–PET10) exhibited a modulus of elasticity of 28 525 MPa and UPV of 4.05 km/s, indicating good-quality concrete. Durability improved significantly, with rapid chloride permeability test values decreasing from 3150 to 2045 Coulombs and water penetration reducing from 22 to 15 mm. Sorptivity decreased from 0.0060 to 0.0041 mm/min0.5, while effective porosity reduced from 15.2% to 12.3%. Abrasion resistance improved by 18.8%, and marine-exposure strength loss decreased from 22.95% to 19.02%. Furthermore, BA and PET reduced cost by 10% and carbon dioxide emissions by 30.6%. Overall, mixes containing 10%–15% BA and 5%–10% PET achieved the best balance of performance, durability and sustainability.

Experimental and ANN-based prediction of the mechanical properties of fly ash geopolymer concrete

Predicting the compressive strength of sustainable concrete with recycled foundry sand using advanced soft computing methods.

Given the environmental challenges posed by the production and disposal of industrial waste, reusing such materials in the construction industry, especially for the development of sustainable concrete, offers an eco-friendly solution and cost reduction. This study investigates the use of waste foundry sand (WFS) as a partial replacement for fine aggregates in concrete. To accurately predict the compressive strength (fc) of WFS-containing concrete, a comparative modeling framework was employed by using one traditional statistical method, Response Surface Methodology (RSM), alongside two advanced soft computing techniques, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP). A dataset consisting of 397 laboratory samples, including various mix design parameters and curing ages as input variables, with fc as the output, was utilized to train and evaluate the models. The results indicate that the RSM model showed the best predictive performance. The fitted model achieved RMSE and MAE values of 4.289 MPa and 3.583 MPa, respectively. Under LOOCV validation, the corresponding errors were RMSECV = 5.40 MPa and MAECV = 4.19 MPa, indicating good generalization capability and stable prediction of compressive strength for concrete containing WFS. The correlation coefficient (R = 0.83) is reported as a secondary performance indicator, indicating a moderate level of agreement between predicted and experimental values. Additionally, sensitivity analysis of input variables indicated that the water-to-cement ratio and superplasticizer-to-cement ratio had the greatest impact on fc, while the WFS-to-cement ratio (WFS/C) and the WFS-to-fine aggregate ratio (WFS/FA) showed a relatively lower influence.

Experimental evaluation and predictive modeling of sustainable concrete incorporating agricultural and industrial waste admixtures

The sustainable use of agricultural and industrial waste materials in concrete requires quantified experimental evidence and interpretable prediction tools for engineering decision-making. This study evaluated concrete containing sugar, cow bone ash, groundnut shell ash and limestone powder and developed interpretable strength models. A 1:2:4 mix used 317 cementitious material, 739 fine aggregate and 1380 coarse aggregate at 0.50 water-to-binder ratio, with 0 to 30% admixture by cement mass. Tests measured slump, initial/final setting time, 7-, 14- and 28-day strength, water absorption and drying shrinkage. The control reached 27.1 MPa at 28 days. Cow bone ash at 8% reached 31.2 MPa, a 15.1% increase while limestone powder at 10% reached 33.3 MPa, a 22.9% increase. These optimum mixtures reduced water absorption from 4.5% to 3.4% and 3.2% and drying shrinkage from 0.050% to 0.043% and 0.038%, respectively. Groundnut shell ash at 10% reduced 28-day strength to 19.5 MPa while sugar at 2% reduced it to 24.5 MPa. Modelling used 144 complete observations screened from 160 records. The artificial neural network achieved test = 0.9877, RMSE = 0.8966 MPa and MAE = 0.7132 MPa; the adaptive neuro-fuzzy inference system achieved = 0.9190, RMSE = 2.3012 MPa and MAE = 1.9406 MPa. 5-fold cross-validation gave = 0.966, RMSE = 1.80 MPa and MAE = 1.27 MPa. Residual diagnostics, Shapley feature influence, partial dependence analysis and an uncertainty band of ± 2 MPa supported interpretation. A web-based model implementation tool was also deployed to support practical preliminary mix assessment. Controlled cow bone ash and limestone powder contents improved strength and durability, while the deployed prediction workflow supports preliminary engineering mix assessment within the tested domain.

Prediction of Sustainable Concrete Strength Incorporating Fly Ash and Recycled Aggregates Using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been used to predict the compressive strength of sustainable concrete as an alternative to large laboratory experiments․ Sustainable concrete mixes were designed by replacing natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) at varying percentages ranging from 0% to 100%․ Ordinary Portland cement was replaced with fly ash at the rate of 0%‚ 5%‚ 10%‚ 15%‚ 20%‚ 25%‚ 30%‚ 35% and 40% to obtain samples․ Twenty-five concrete mixes were tested for compressive strength at 3‚ 14 and 28 days․ In contrast‚ for moderate fly ash replacement levels (10-20 percent)‚ strength increased at later ages due to pozzolanic reactions․ At high fly ash replacement levels‚ strength development was delayed during the curing period․ Increasing RCA content decreased compressive strength because of the presence of unhydrated mortar‚ greater porosity‚ lower bonding between the aggregate and paste matrix‚ and greater water absorption compared to normal aggregate․ An ANN model has been developed to predict the concrete strength‚ using five parameters as input (NCA content‚ RCA content‚ cement content‚ fly ash content and curing age) and the concrete compressive strength as an output parameter․ Based on the seventy-five experimental results‚ the developed model is highly accurate‚ with R²-value of 0․9935 and RMSE (Root Mean Square Error) of 0․6465 MPa․ These results show that the ANN can successfully predict the sustainable concrete compressive strength․ The proposed model is very useful for mix proportion optimization with considerable reductions in the time‚ cost‚ and effort of experimental studies․