Jong-Soo Jeon

The prediction of failure mode of columns is critical in deciding the operational and recovery strategies of a bridge after a seismic event. This paper contributes to the critical need of failure mode prediction for circular reinforced concrete bridge columns by exploring the capabilities of machine learning methods. Three types of failure mode such as flexure, flexure-shear, and shear are considered in this study, and 311 specimens are compiled from experimental studies on the circular columns.

Summary Recent efforts of regional risk assessment of structures often pose a challenge in dealing with the potentially variable uncertain input parameters. The source of uncertainties can be either epistemic or aleatoric. This article identifies uncertain variables exhibiting strongest influences on the seismic demand of bridge components through various regression techniques such as linear, stepwise, Ridge, Lasso, and elastic net regressions. The statistical results indicate that Lasso regress

Summary A framework for the generation of bridge‐specific fragility curves 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 resimulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of ea

Summary Current seismic design codes and damage estimation tools neglect the influence of successive events on structures. However, recent earthquakes have demonstrated that structures damaged during an initial event (mainshock) are more vulnerable to severe damage and collapse during a subsequent event (aftershock). This increased vulnerability to damage translates to increased likelihood of loss of use, property, and life. Thus, a reliable risk assessment tool is required that characterizes th

SUMMARY This paper proposes a new set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine‐learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR). In order to achieve high‐fidelity regression models with reduced model errors and bias, this study constructs extensive experimental databases for reinforced and unreinforced concrete joints by collecting existing beam‐column joint subassem

This paper addresses the application of a Bayesian parameter estimation method to a regional seismic risk assessment of curved concrete bridges. For this purpose, numerical models of case-study bridges are simulated to generate multiparameter demand models of components, consisting of various uncertainty parameters and an intensity measure (IM). The demand models are constructed using a Bayesian parameter estimation method and combined with limit states to derive the parameterized fragility curv