"Bridges At Risk: Multi-Hazard Fragility Analysis Using Time History Analysis And Artificial Intelligence Models"

Ashwini Satyanarayana; Dushyanth V Babu R; Sheethal H M

doi:10.29284/4c7enx84

Authors

Ashwini Satyanarayana Department of Civil Engineering, JAIN (Deemed to be University), Bengaluru, India
Dushyanth V Babu R Department of Civil Engineering, JAIN (Deemed to be University), Bengaluru, India
Sheethal H M Department of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru -560111

DOI:

https://doi.org/10.29284/4c7enx84

Keywords:

Seismic Performance, Bridge Structure, Ground Movements, Time History Analysis, Regression Plot.

Abstract

Bridges are crucial for facilitating efficient commerce and transportation between cities and across the country, making them a key component of highway transportation networks. The substantial financial losses caused by recent earthquakes have highlighted the susceptibility of essential bridge components and the necessity for precise evaluation of their seismic performance. In addition to earthquakes, flooding is another significant force that can greatly harm bridge structures. Consequently, there has been an increased focus on multi-hazard vulnerability assessments over the past few decades. Many older bridges constructed prior to 1970 are displaying signs of wear that are exacerbated by factors such as climate change. When these bridges were originally built, seismic design standards did not provide the comprehensive guidelines needed to ensure their strength and resilience. As a result, many existing bridges often do not satisfy the criteria for structural integrity and load capacity, highlighting the critical need for enhancements to extend their lifespan and ensure safety in today's dynamic environmental conditions. If seismic forces are not sufficiently mitigated, bridges may undergo significant lateral shifts and ground movements that can lead to structural damage, disruptions in service, and potentially disastrous outcomes. The response dynamics of a structure during intense seismic events are heavily influenced by the properties and behavior of the surrounding soil, in addition to the structure’s superstructure. Dynamic analyses, such as Time History Analysis, are performed to evaluate the bridge. Fragility analysis assesses the likelihood that a structure or its components will reach a certain level of damage based on a specific ground motion intensity measure. In this study, artificial neural networks (ANNs) are utilized to address uncertainties surrounding influencing factors. The input and output of the ANN models have been identified as the significant characteristics and fragility parameters of key components, respectively.

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