Evolutionary Polynomial Regression Algorithm with Uncertain Variables: Two Case-Studies in the Field of Civil Engineering

Data-driven approaches and calibration techniques for mathematical models, starting from observed data, are attracting more and more interest in the field of civil engineering. Among them, evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that combines genetic algorithms (GAs) and regression strategies. However, the difficulties and uncertainties inherent in the method have pointed out how the implementation of proper computational methods together with the use of recent and qualified databases of experimental data are essential to carry out reliable formulations. In this framework, this paper explores a new robust EPR approach able to remove potential outliers and leverage points often occurring in biased dataset and simultaneously accounting for the effects of probabilistic uncertainties. Uncertainties are incorporated in the EPR methodology by adopting the direct perturbation method. In particular, it is shown the importance to set the parameters representative of experimental and analytical dispersions on the basis of the characteristics of the database in terms of homogeneity. With this purpose, two different case-studies are analyzed, dealing with the shear capacity of RC beams without stirrups and the compressive strength of cement-based mortar specimens, respectively. Finally, the best capacity equations are selected and discussed.