Empirical-based support vector machine method for seismic assessment and simulation of reinforced concrete columns using historical cyclic tests. (15th June 2021) - Liu, Zhenliang; Guo, Anxin
Highlights: An EM-based SVM method was proposed for seismic assessment of RC columns. The common SVM training process may not reflect the effects of certain inputs. The developed model was better than the common SVM and semi-empirical methods. The engineering applications of the developed SVM model were demonstrated. Abstract: Support vector machine models are generally considered as "black box" procedures, which sometimes render them unexplainable and prone to over-fitting or under-fitting in absence of sufficient data. In this study, an empirical-based support vector machine (EM-based SVM) method is proposed to model the complicated relationships among the basic characteristics of reinforced concrete columns and their corresponding critical performance metrics on hysteretic curves. Moreover, a database of historical pseudo-static cyclic tests is established for model training and test after rigorous screening of specimens. The developed model is then validated by comparing with the results of common SVM method and semi-empirical formulas, as well as pseudo-static and shake table tests. Finally, its applicability and effectiveness for seismic damage assessment and simulation are explored, which demonstrates its potential value in engineering applications.