Chul‐Woo Kim

This paper presents a methodology for damage identification based on a time domain approach, considering the coupling vibration between a bridge and a moving vehicle, including the effect of roadway surface roughness. The fundamental idea of the proposed method is to identify damage directly from changes in the element stiffness using a pseudo-static formulation derived from the equations of motion for coupling vibration. The element stiffness index (ESI), which indicates the ratio of a damaged

This study is intended to investigate the seismic response of steel monorail bridges using three-dimensional dynamic response analysis. We particularly consider monorail bridge–train interaction when subjected to ground motion that occurs with high probability. A monorail train car with two bogies with pneumatic tires for running, steering and stabilizing wheels is assumed to be represented sufficiently by a discrete rigid multi-body system with 15 degrees of freedom (DOFs). Bridges are consider

Ambient and vehicle-induced bridge vibration tests are two important ways of collecting data for the purpose of system identification and performance evaluation of bridges. A common bridge form that has been very popular over the past number of decades is the steel truss bridge; this paper presents the vibration data of a steel truss bridge situated in Japan: the Old ADA bridge. The target bridge was built in 1959 and removed in 2012. Prior to the removal of the bridge, both ambient and vehicle-

This paper investigated the feasibility of the pseudo-static damage identification method derived from a bridge–vehicle interaction system through a moving vehicle laboratory experiment. The element stiffness index, defined as the ratio of flexural rigidity of a damaged member to that of an intact member, serves as the damage indicator. Three vehicle models and two travelling speeds were considered in the experiment to examine the effect of vehicle's dynamic characteristic and travelling speed o

Damage detection is one important target in structural health monitoring (SHM). Vibration-based damage detection has attracted more attention in the past decades by tracking the modal parameter changes of objective structures. This paper presents the work on developing a novel Bayesian fast Fourier transform (FFT) method for damage detection using the Bayes factor based on ambient vibration data. Based on the properties of FFT data, the likelihood function and prior probability density function

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters ar

Health condition monitoring of bridge structures is attracting considerable attention, conventionally relying on visual inspection, and measurement-based methods which involve sensors installed directly on bridges. In recent years, drive-by monitoring methods that treat moving vehicles as moving sensors have been proposed as alternatives; these methods aim to be low-cost, mobile, and target fast bridge condition screening. This study addresses the current lack of sufficient experimental verifica

5th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-5), Cancun, Mexico, 11-15 December, 2011

Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). Based on Fourier neural operator, this study proposes VINO (Vehicle–Bridge Interaction Neural Operator) to serve as a surrogate model of bridge structures. VINO learns mappings between structural response fields and damage fields. In this study, vehicle–bridge interaction (VBI)–finite element (FE) data set was