ANN trained by BBO for modeling of fly ash cementitious systems with high range water reducing admixtures

This study aims to develop artificial intelligence (AI) models for predicting the compressive strength and flow value of cementitious systems containing fly ash, influenced by various high-range water-reducing admixtures (HRWRAs) that differ in molecular weight and chain length. A database comprising 180 mixes was created, encompassing cement and fly ash dosages, HRWRA characteristics (including molecular weight, main and side chain lengths) curing period, and flow time. Two AI-based modelling approaches were employed: a classical artificial neural network (ANN) and a new hybrid model that integrates ANN with biogeography-based optimisation (ANN-BBO). The modeling results showed that the hybrid model achieved a compressive strength performance with an R² of approximately 0.99 and an RMSE of around 1.37 MPa, while the single ANN model attained an R² of about 0.91 and an RMSE of 4.40 MPa. For flow value prediction, the ANN-BBO model also demonstrated high accuracy (R² ≈ 0.98; RMSE ≈ 0.32 cm). Furthermore, the ANN-BBO model reduced the prediction error by approximately 60% across the evaluation criteria compared to the single ANN model, highlighting its enhanced performance. The importance of the input variables indicated that curing time and cement content have the greatest impact on compressive strength, while flow time and the molecular weight of the HRWRA significantly influence the flow value. Since AI models rely solely on virtual trials, they significantly reduce laboratory time and material usage while aiding in the design of mixes with lower water-to-binder ratios and higher fly ash content, which ultimately helps to reduce the CO₂ footprint. The proposed models provide a practical route to low-clinker, FA-rich mix designs that satisfy strength/workability targets with less cement, supporting embodied-carbon reductions and straightforward integration into ready-mix/precast quality-control workflows.