Seismic response prediction of reinforced concrete buildings through nonlinear combinations of intensity measures

A widespread approach for the prediction of the structural response as function of the ground motion intensity is based on the Cloud Analysis: once a set of points representing the engineering demand parameter (EDP) values is obtained as function of the selected seismic intensity measure (IM) for a collection of unscaled earthquake records, a regression analysis is performed by assuming a specific functional form to correlate these variables. Within this framework, many studies have been devoted so far to evaluate the effectiveness of several IMs in estimating the EDPs through intrinsically linear functional forms, but it is still unknown to what extent the use of the linear regression analysis affects the quality of the final results. This paper is intended to provide an answer to such question by means of the calibration of suitable nonlinear combinations of scalar IMs, whose statistical performances are compared with those obtained by using the functional form usually adopted for linear regression-based calibrations. Specifically, the Evolutionary Polynomial Regression technique is adopted to calibrate nonlinear regression models for the prediction of maximum inter-story drift ratio and maximum floor acceleration. The comparative analysis is performed for fixed-base and base-isolated reinforced concrete buildings subjected to ordinary or pulse-like ground motion taking into account accuracy, complexity, efficiency and sufficiency. Final results demonstrate that the linear regression analysis is suitable for fixed-base reinforced concrete buildings, but nonlinear regression models provide better estimates. On the other hand, the linear regression analysis can introduce a significant bias in the seismic response prediction of base-isolated buildings, and nonlinear regression models are deemed more appropriate.