Machine Learning-Based Prediction of Operability for Friction Pendulum Isolators Under Seismic Design Levels

Abstract

Within the scope of the study, the parameters of friction pendulum-type (FPS) isolators used or planned to be used in different projects were evaluated specifically for the project and its location. The evaluations were conducted within a performance-based seismic design framework using displacement, re-centering, and force-based operability criteria, as implemented through the Türkiye Building Earthquake Code (TBDY) 2018. The friction coefficient and radius of curvature were evaluated, along with the lower and upper limit specifications determined according to TBDY 2018. The planned control points were the period of the isolator system, the isolator re-centering control, and the ratio of the base shear force to the structure weight. Within the scope of the study, isolator groups with different axial load values and different spectra were evaluated. A dataset was prepared by using the parameters obtained from the re-centering, period, and shear force analyses to determine the conditions in which the isolator continued to operate and those in which conditions prevented its operation. Machine learning models were developed to identify FPS isolator configurations that do not satisfy the code-based operability criteria, based on isolator properties, spectral acceleration coefficients corresponding to different earthquake levels, mean dead and live loads, and the number of isolators. The resulting Bagging model predicted an isolator’s operability with a high degree of accuracy, reaching 96%.