AI-based prediction for the strength, cost, and sustainability of eggshell and date palm ash-blended concrete

Abstract Eggshell powder (ESP) and date palm ash (DPA) are increasingly used as sustainable cement substitutes in cementitious composites. This study used multi-expression programming (MEP) to develop prediction models due to its advantage of yielding model equations. The attributes of ESP and DPA-modified concrete chosen for modeling include compressive strength (C-S), eco-strength (E-C-S), and cost-strength ratio (C-S-R). Hyperparameters in MEP were fine-tuned to get the maximum accuracy for predictions. The models were validated using R 2 and statistical checks and analyzing the variance among predictions and real values. The MEP models were noted to be exact in estimating C-S, C-S-R, and E-C-S with an R 2 of 95, 93, and 92%, respectively, indicating good agreement with actual data. Additionally, the ±20% index analysis indicated that all values fall within the acceptable range, validating the model’s reliability. The mathematical expression-based MEP prediction models developed in this study can be applied to future C-S, C-S-R, and E-C-S predictions in ESP–DPA-modified concrete. These models are designed to operate with a predetermined set of input parameters and are incompatible with a variable set of inputs. Additionally, it is imperative to maintain consistency in the units of inputs to obtain precise predictions from the constructed models.