Recent years have witnessed a burgeoning interest in sustainable, eco-friendly, and cost-effective construction materials for civil engineering projects. Soilcrete, an innovative blend of soil and cement, has gained significant acclaim for its versatility and effectiveness. It serves not only as grout for soil stabilization in corrosive environments like landfills and coastal regions but also as a reliable material for constructing structural elements. Understanding the mechanical properties of soilcrete is crucial, yet traditional laboratory tests are prohibitively expensive, time-consuming, and often imprecise. Machine learning (ML) algorithms present a superior alternative, offering efficiency and accuracy. This research focuses on the application of the adaptive neuro-fuzzy inference system (ANFIS) algorithm to predict the uniaxial compressive strength (UCS) of soilcrete. A total of 300 soilcrete specimens, crafted from two types of soil (clay and limestone) and enhanced with metakaolin as a pozzolanic additive, were meticulously prepared and tested. The dataset was divided, with 80% used for training and 20% for testing the model. Eight parameters were identified as key determinants of soilcrete's UCS: soil type, metakaolin content, superplasticizer content, shrinkage, water-to-binder ratio, binder type, ultrasonic velocity, and density. The analysis demonstrated that the ANFIS algorithm could predict the UCS of soilcrete with remarkable accuracy. By combining laboratory results with ANFIS model predictions, the study identified the optimal conditions for maximizing soilcrete's UCS: 11% metakaolin content, a 0.45 water-to-binder ratio, and 1% superplasticizer content.

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na2SiO3) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)-the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material's thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R2 of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R2 of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R2 of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.