Synergistic Effects of

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

This study investigates the performance of sustainable concrete using Over-Burnt Brick Waste (OBBW) as a partial replacement for natural Coarse Aggregates (CA), with Class C fly ash (F) as a supplementary cementitious material. Concrete mixtures were prepared with OBBW replacement levels ranging from 5% to 55% at a constant 10% F content. Mechanical properties were evaluated experimentally, and statistical analysis and machine learning models were used to assess the relationships among the mix design parameters. The experimental results indicate that OBBW replacement levels of up to 50% improved mechanical performance. Specifically, the optimized concrete mix achieved a Compressive Strength (CS) of 34.65 MPa, representing a 22.7% increase over the control mix (28.24 MPa). Furthermore, flexural strength increased by 4.7%, from 3.4 MPa to 3.56 MPa. The findings demonstrate that the use of OBBW and fly ash enables the production of high-performance, eco-friendly concrete.

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.

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.

Investigation of the effect of using recycled aggregate, metakaolin, and fly ash on the performance of structural lightweight concrete

This study investigated the effects of recycled aggregate (RA) substitution and fly ash (FA) and metakaolin (MK) as cement replacements on the mechanical and durability properties of lightweight concrete produced with natural perlite aggregate (NPA), as well as their performance under high temperatures. For this purpose, NPA was replaced with RA at rates of 0%, 20%, and 40% in both coarse and fine aggregate fractions. Four different mixtures were produced for each RA content: a control mixture, 10% FA, 10% MK, and 10% FA + 10% MK. Unit weight, compressive strength, splitting tensile strength, sorptivity, water absorption and electrical resistivity tests were performed on the produced concretes. In addition, to determine the behavior of concretes under high temperatures, weight losses and compressive strength losses were determined in concrete samples subjected to temperatures of 250℃, 500℃ and 750℃, and visual evaluations were also carried out. Increasing the RA content generally reduced the mechanical properties, durability performance and high-temperature resistance of the concretes. However, extending the curing period from 28 to 90 days and the use of mineral admixtures helped to compensate for these effects. Higher RA content slightly reduced mechanical properties, but the %20RA + %10FA + %10MK mix is the most balanced option as it improves durability, uses waste materials, and reduces cement use.

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

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․