Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques

TL;DR

This paper introduces a machine learning-based framework using random forests and stripe-based methods to efficiently generate and update bridge fragility curves without relying on traditional assumptions, demonstrated on California concrete bridges.

Contribution

It presents a novel, computationally efficient approach for bridge fragility analysis that accounts for uncertainties and avoids traditional demand model assumptions.

Findings

The methodology reduces computational effort compared to traditional simulations.

It identifies the importance of various uncertain variables in seismic demand.

Fragility curves can be integrated into risk assessment tools like HAZUS.

Abstract

A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive re-simulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case studies of multi-span concrete bridges in California. Geometric, material and structural uncertainties are accounted for in the generation of bridge models and fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained the proposed methodology curves can be deployed in risk assessment platform such as HAZUS for regional loss estimation.