A multifaceted comparative analysis of incremental dynamic and static pushover methods in bridge structural assessment, integrated with artificial neural network and genetic algorithm approach
Ashwini Satyanarayana, V. Sindura, L. Geetha, Rakesh Kumar, Mohd Asif Shah, Mary Subaja Christo
Abstract
This article compares two widely used techniques for assessing the seismic performance of bridge structures: incremental dynamic analysis (IDA) and static pushover analysis (SPA). Both methods provide frameworks for evaluating structural deformation capacity and strength, emphasizing the critical importance of safety and resilience during seismic events. SPA is characterized by its simplicity, applying nonlinear loading until a predefined displacement is achieved. In contrast, IDA employs a more comprehensive approach, subjecting the structure to a series of scaled ground motion recordings to analyze its behavior across varying earthquake intensities. The primary objective of this study is to evaluate the dependability and accuracy of SPA and IDA in predicting bridge seismic performance. This is accomplished by thoroughly investigating a standard bridge sample, therefore allowing a complete comparison of the two methods. The outcomes seek to further understanding of seismic performance evaluation and offer direction for engineers selecting appropriate techniques for bridge analysis design against seismic hazards. Bridge engineers can benefit greatly from computational methods like genetic algorithms (GA) and artificial neural networks (ANN), which can forecast results based on input parameters. By learning from data, these techniques allow for precise predictions of structural behavior, damage identification, and design optimization. Applying these methods improves the analysis of intricate bridge systems, leading to safer structures by enhancing evaluations of durability, load-bearing capacity, and possible failure points. The results of static pushover analysis and incremental dynamic analysis (IDA) are predicted and compared in this work using ANN and GA, which offers a deeper understanding of their predictive capabilities.