Estimating the Compressive Strength of Lightweight Foamed Concrete Using Different Machine Learning-Based Symbolic Regression Techniques

The development of concrete with excellent water and frost resistance providing high level of sound and thermal insulation has triggered the formulation of foamed concrete. However, multiple laboratory studies are required to produce reasonable data to design the relevant codes and mathematics with which design of mixes is made easier at low cost. In this research paper, the artificial intelligence (AI)-based symbolic regression technique estimation of the compressive strength of foamed concrete has been reported. Foamed concrete has been a subject of serious research in sustainable built-environment due to its lightweight and structural functionality. In this research work, data gathering method was applied to gather a globally representative data base comprising concrete density to water density (concrete density g/cm3) (γ/γw), water-cement ratio (W/C), and sand-cement ratio (S/C) as input variable and the compressive strength (Fc) as the study output. The dimensionless factors have been derived to eliminate data handling complexities and improve model performances. The 230 data entries from foamed concrete mixes were partitioned into 75% and 25% for training and validation data, respectively. At the end of the model execution, it was found that the response surface methodology (RSM) produced a symbolic closed-form equation like the genetic programming (GP), evolutionary polynomial regression (EPR), and the group method of data-handling-neural network (GMDH-NN). Even though the RSM closed with a minimum error, the GP, EPR and GMDH-NN were faster in runtime. The overall outcomes show that the GP outclassed the EPR, RSM and the GMDH-NN, though with minor margin. Meanwhile the EPR produced the highest outliers from the ±25% test of accuracy envelope. Overall, the present models outperformed those reported in the literature due the parameter reduction through dimensionless factors derivation and provided a decisive model to predict the Fc of foamed concrete.