ML prediction and ANN-PSO based optimization for compressive strength of blended concrete

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCM). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete that was developed for ground-granulated blast furnace slag (GGBS) along with Alccofine 1203 (AF). Boosting regressions of Gradient Boosting Regression (GBR), Extreme Gradient Boosting Regression (XGBR) and Light Gradient Boosting Regression (LGBR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 120 experimental conditions. Moreover, a comparative study was performed among GBR, XGBR and LGBR to benchmark the proposed model against a given combination of features (parameters/factors) and compressive strength. By using CPC-X Neural Power software, the experimental conditions were optimized using the Particle Swam Optimization (PSO) algorithm after the model was created by the Artificial Neural Network (ANN). Of the three boosting algorithms, the smallest errors along with the largest coefficient of determination were observed in the GBR algorithm and the optimized values of CS is 77.4 MPa and its optimized process factors (Alccofine, GGBS, Cement, C.A, F.A, SP dosage and days) were 24.77 kg/m3, 24.63 kg/m3, 534.92 kg/m3, 1100.08 kg/m3, 628.64 kg/m3, 4.20 lit/m3 and 29.03 days respectively.