Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gunvanth, G.; Bhalkikar, Aniket; Kollerathu, Jacob Alex Title A name given to the resource New Empirical Equation for Fundamental Time Period of RC Moment-Resisting Frame Buildings Using Machine Learning Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering;Volume;716 LNCE;pp.643-654 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9712-0_45" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9712-0_45</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033881368?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033881368?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gunvanth G., NFSU, Gandhinagar, India; Bhalkikar A., Christ University, Bangalore, India; Kollerathu J.A., Christ University, Bangalore, India Description An account of the resource The fundamental time period of reinforced concrete (RC) buildings is a critical parameter in structural engineering, influencing their dynamic behavior and response to seismic and wind loads. This study aims to propose a new empirical formula for estimating the fundamental time period of RC buildings through regression analysis. Leveraging the SAP2000 API with VBA code, a dataset comprising 200 two-dimensional RC building models was rapidly generated, allowing for efficient exploration of various building configurations. Modal analysis was conducted for each model to determine the fundamental time period, and regression analysis was performed using both multiple linear regression and curve estimation regression techniques. The input parameters included total building height and base dimensions, while the output variable was the fundamental time period obtained from SAP2000 results. Multiple linear regression yielded two best-fit models, while curve estimation regression produced logarithmic and exponential models. The proposed models were compared with the fundamental time period values obtained from SAP2000 results and those calculated using the formula specified in the Indian Standards (IS) code. Further the results obtained are used to develop a machine learning model that can be used to estimate the time period of RC structures for a given height. The model is chosen after estimating the coefficient of regression for various individual machine learning algorithms and ensemble algorithms. This research contributes to the advancement of structural engineering by providing a systematic approach to developing empirical formulas tailored to RC buildings. The proposed formula, enabled by the automation capabilities of the SAP2000 API, offers a more accurate and reliable method for estimating the fundamental time period, facilitating improved seismic design and analysis practices. Further validation and verification of the formulas performance using additional datasets and real-world case studies are recommended to enhance its applicability and robustness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Empirical formulas; Fundamental time period; Prediction improvement; Seismic performance Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23662557; ISBN: 978-981969711-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online