Machine learning based modelling for estimation of the fundamental time period of precast concrete structures using computer programming

This research investigated the capability of machine learning approaches to evaluate the fundamental time period (FTP) of precast concrete structures. Data set consisting of 288 models with shear wall and beam-column frame structures. The 288 models were analysed using Etabs software and Rstudio. Input parameters consisted of the height of the building, number of bays, length and breadth of the building, cracked or uncracked section, number of storeys and frame type on the FTP of precast concrete structures. Out of 288 models, for testing 108 arbitrary selected models were used and the remaining 180 models were used for training. Linear (LRF), polynomial (PLF) and radial basis (RBF) kernel functions were used for machine learning approach i.e support vector machines (SVM) and gaussian process (GPR). Evaluation of results suggests that linear function-based support vector machines performed well as compared to gaussian process regression. The accuracy of the machine learning approaches was verified through comparison with the available equations to evaluate the FTP in literature.