Fundamental Period of Steel Braced RC Structures
This dataset comprises 17,280 unique building models, with each model characterized by different building parameters and corresponding eigenvalue modal analysis results for both Concentrically Braced Frames (CBFs) and Eccentrically Braced Frames (EBFs). Building parameters taken into consideration include, but are not limited to, the type and installation position of bracing, the number of storeys, the dimensions of bays in X and Y directions, the depth of the beam, the width of the column, the type of bracing, and the properties of the materials used. Additionally, the data is presented in a tabular CSV format, which allows for easy access and manipulation of the data. Each row in the CSV file represents a unique building model, with the corresponding parameters and fundamental vibrational period specified in the columns. This format facilitates easy navigation, understanding, and application of the data. In conclusion, this dataset serves as an extensive tool for future research in structural engineering, particularly in the area of seismic behaviour. It paves the way for a more nuanced understanding of steel-braced reinforced concrete structures and aids in the development of sophisticated predictive models. Its significant potential for reuse in diverse contexts, from refining design methodologies to developing machine learning models, underlines its broader value to the academic and professional community.
Steps to reproduce
The data for this comprehensive study was gathered using a Python-based script that automated the ETABS Application Programming Interface (API). ETABS is a renowned software widely recognized for its proficiency in structural analysis and design of buildings. The ETABS API was harnessed for automation, enabling efficient generation, analysis, and extraction of the necessary output data from structural models. The automation of the process allowed for efficient generation of a multitude of structural models and execution of eigenvalue modal analyses to determine the fundamental period of vibration. This methodology led to a reduction in potential human error, thereby increasing the accuracy, reproducibility, and efficiency of the data generation process.