AI-driven sustainable strength prediction and experimental evaluation of high-performance fiber-reinforced concrete incorporating metakaolin.

This work developed an enhanced model to examine the High-Strength Fiber-Reinforced Concrete (HSFRC) that is replaced with 10% metakaolin as a sustainable cement replacement and four types of fibers at different dosages. Here, four types of fibers, Steel Fiber (SF), Glass Fiber (GF), Nylon Fiber (NF), and Polypropylene Fiber (PPF) wereincorporated, and concrete mixtures were designed with varying proportions of these Fibers. A 10%replacement of cement with Metakaolin was used as a Supplementary Cementitious Material (SCM). An M60 grade mix was selected, and the water-cement ratio (W/C) was maintained at 0.32.The most effective mix configuration was identified as High StrengthConcrete (HSC) (Concretewith 10% Metakaolin) combined with 1% steel fiber by cement weight.Then, the deep learning framework named Adaptive Pyramid Dilated Dense Long Short-Term Memorywith Sparse Attention (A-PDDLSTM-SA) is developed for predicting the compressive strength, split tensile strength, and flexural strength of concrete. To further optimize its performance, the model’s parameters are fine-tuned using the Updated Random Number-based Hiking Optimization Algorithm (URN-HOA). The manually collected experimental data is used for both training and evaluation of the predictive model, ensuring high reliability and generalizability across various mix compositions.The Mean Error Percentage (MEP) of the proposed model is 25.747 and the accuracy is 96.13%. Thus, it shows that the developed model proved notable improvement over other existing approaches in enhancing the accuracy and reducing the reliance on the wide-ranging destructive testing, providing a consistent supporting tool to develop enhanced and sustainable concrete mixes.