Seismic-Resistant Design Using Advanced Computational Models (International Journal of Engineering & Extended Technologies Research (IJEETR), 2023)
Seismic-resistant design is critical for ensuring the structural integrity and safety of buildings and infrastructure in earthquake-prone regions. Traditional design methods often rely on empirical formulations and simplified analytical models, which may not fully capture the complex dynamic behavior of structures during seismic events. This study explores the application of advanced computational models, including finite element analysis (FEA) and nonlinear dynamic simulations, to develop more robust seismic-resistant designs. The research integrates state-of-the-art modeling techniques with real-world seismic data to assess structural performance under various earthquake scenarios. Using these computational tools, the study evaluates the effectiveness of different structural configurations, material properties, and damping systems in mitigating seismic forces. The models incorporate soil-structure interaction and material nonlinearity to better predict failure mechanisms and optimize design parameters. Results demonstrate that advanced computational models can significantly improve the accuracy of seismic response predictions compared to conventional methods. The findings provide valuable insights into the design of earthquake-resistant structures, promoting safer construction practices and reducing economic losses. This paper also discusses the limitations of current computational approaches and suggests directions for future research, including the integration of machine learning algorithms and real-time monitoring data for adaptive seismic design. The overall contribution highlights the potential of computational advancements to revolutionize seismic engineering, enhancing resilience and sustainability in urban development.
Keywords: Seismic-resistant design; Computational modelling; Finite element analysis (FEA); Nonlinear dynamic simulation; Soil-structure interaction