New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs

Abstract

The importance of designing safe and economic structures in seismically active areas is of great importance. Thus, developing tools that would help in accurately predicting the dynamic properties of buildings is undoubtable a crucial objective. One of the parameters that significantly affects the seismic design of any structure is the fundamental period that is used to compute the seismic forces. It is well documented that the current design formulae for the prediction of the fundamental period of reinforced concrete buildings are simplistic and often fail to capture accurately their expected natural frequency. In addition, the design formulae do not have the ability to account for the soil-structure interaction (SSI) effect that, in some cases, significantly affects the natural frequency of buildings due to the additional flexibility induced by the soft soil. In this research work, a computationally efficient and robust 3D modeling approach is used for the modal analysis in order to investigate the accuracy of different design formulae in predicting the fundamental period of reinforced concrete buildings with and without SSI effects. In this context, 3D detailed modeling is used to generate a dataset that consists of 475 modal analyses, which is subsequently used to train and produce three predictive formulae using a higher-order, nonlinear regression modeling framework. The developed fundamental period formulae were validated through the use of 60 out-of-sample modal results and they are also compared to other existing formulae in the international literature and design codes. According to the numerical findings, the proposed fundamental period formulae are found to have superior predictive capabilities for the under-study types of buildings.