Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms Nathan Carstens 1 , George Markou 1 and Nikolaos Bakas 2 1 Department of Civil Engineering, University of Pretoria, South Africa 2 Department of RnD, RDC Informatics, Athens, Greece Keywords: Machine Learning Algorithms, Fundamental Mode Formulae, Modal Analysis, Soil-structure Interaction, Finite Element Method, Reinforced Concrete, Hybrid Modelling. Abstract: With the development of technology and building materials, the world is moving towards creating a better and safer environment. One of the main challenges for reinforced concrete structures is the capability to withstand the seismic loads produced by earthquake excitations, through using the fundamental period of the structure. However, it is well documented that the current design formulae fail to predict the natural frequency of the considered structures due to their inability to incorporate the soil-structure interaction and other features of the structures. This research work extends a dataset containing 475 modal analysis results developed through a previous research work. The extended dataset was then used to develop three predictive fundamental period formulae using a machine learning algorithm that utilizes a higher-order, nonlinear regression modelling framework. The predictive formulae were validated with 60 out-of-sample modal analysis results. The numerical findings concluded that the fundamental period formulae proposed in this study possess superior prediction ability, compared to all other international proposed formulae, for the under-studied types of buildings. 1 INTRODUCTION The soil-structure interaction (SSI) phenomenon is a typical structural and geotechnical engineering issue, still open regarding its practical applications. Further investigation is required to develop simplified but reliable methods to account for such a phenomenon in routine structural analyses (Ceroni et al., 2012). In calculating the appropriate seismic loads, the fundamental period serves as one of the most critical dynamic characteristics. In the event of a seismic excitation, the interaction between the superstructure (building) and substructure (soil) becomes critical as it commences to alter the distribution of stresses and strains within the superstructure, which alters the expected results (Mourlas et al., 2019). It is well known that computing the fundamental mode of fixed-base structures through design code formulae has its challenges (Mourlas et al., 2019). Furthermore, some shortcomings exist in the stiffness distribution of the structure due to a lack of adequate consideration of the effects of shear walls, especially in the Eurocode 8 design code (Gravett et al., 2019). These considerations can cause a considerable amount of over or under designing of reinforced concrete (RC) structures, which can lead to inadequate designs liable to seismic conditions. Thus, it is crucial to establish a design tool that can successfully predict the dynamic properties of a variety of different RC structures. It is usually not in favour of safety to analyse the response of a fixed-base structure by neglecting the SSI effect. In some cases, codes provide seismic design provisions by reducing the base shear of the fixed-base structures. In others, they suggest performing advanced analysis to investigate the overall effect (Mourlas et al., 2020). As a result, there is a need for more accurate design expressions for RC structures that can accurately predict their fundamental period while accounting for SSI effects. When it comes to the SSI effect, the reaction of a building to a seismic event is evaluated in conjunction with the compressibility of its surrounding soil. The flexibility of the soil can impact its stress distribution and displacement profiles, which can be distinguished from standard fixed-base systems (Saadi, 2018, Markou et al., 2018). Carstens, N., Markou, G. and Bakas, N. Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms. DOI: 10.5220/0010984500003116 In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - Volume 2, pages 647-652 ISBN: 978-989-758-547-0; ISSN: 2184-433X Copyright c  2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 647