SHM‐integrated bridge reliability estimation using multivariate stochastic processes

Abstract

SUMMARYDetermination of response characteristics using probabilistic approaches is essential to deal with high level of load and resistance uncertainties in civil engineering structures. Multi-span bridges present a significant problem regarding the varying nature of seismic loads on different bridge piers. With an emphasis on comparison of multi-support excitation with the conventional uniform excitation, this paper aims at providing a framework to evaluate probabilistic seismic response and estimate reliability of bridges under multi-support excitations simulated by multivariate stochastic processes. Moreover, the framework integrates the experimental data of a multi-support seismic shaking table test of a multi-span bridge structure as well as structural health monitoring (SHM) findings based on vibration measurements. The results demonstrate the importance of the multivariate stochastic processes, therefore, multi-support excitation, on estimating seismic behavior of multi-span bridges. Furthermore, this study proves the significance of structural parameter updating with respect to the evaluation of structural reliability. It is observed that the difference between the SHM-integrated and the conventional reliability estimation results vary according to the change in bedrock conditions.

Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems

“…This is conducted by developing an objective function quantifying the error between a model and the reference modal parameter values. In the former studies, the authors adopted Least Square Method (LSM) to formulate the objective function [46,47] whereas different approaches are present in the literature [55,56], in this study, the Figure 6 shows the modeling uncertainties taking place without the necessary documentation. To summarize, tubular structural member section dimensions, distributed mass due to non-structural components, and support restraints all contribute to the modeling uncertainties and will be determined throughout the FEM updating process.…”

“…This is conducted by developing an objective function quantifying the error between a model and the reference modal parameter values. In the former studies, the authors adopted Least Square Method (LSM) to formulate the objective function [46,47] whereas different approaches are present in the literature [55,56], in this study, the objective function is structured in terms of error between the simulation and the experimental results and multiplication of multiple modal parameter errors. To specify, the objective function, which is a function of the support restraints, stiffness and mass distributions, is formulated as Figure 6.…”

“…This can be performed by utilizing the modal identification results, calibrating mathematical models, and obtaining the probabilistic failure distribution under a wide range of strong ground motions. Eventually, using identification results as model calibration tools, civil infrastructures' seismic response and structural reliability can be estimated [46,47] to provide the decision makers with the necessary information.…”

Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

“…Studies have shown that the modal identification results gathered at different damage states return different structural reliability values, which is a strong measure of the structural health. 3,4 As these monitoring results are collected frequently, in the long run, environmental effects can also be eliminated from the identification results, and the remaining outcomes will purely depend on structural deterioration. 71 To summarize, if correct long-term monitoring strategies are adopted and modal identification results are observed accordingly, damage characteristics can be captured from the mobile pedestrian data.…”

“…3,4 As new mobile, [5][6][7][8] heterogeneous, [9][10][11][12] smart, [13][14][15][16] wireless, and distributed [17][18][19][20][21][22] sensing technologies emerge, SHM systems have become more practical, cost-effective, and sustainable not only for laboratory but also for field applications.…”