Abstract
Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.
Highlights
The authors of this paper developed the method for moving load identification for bridge structures and verified the results numerically and experimentally, which are published in the reference [32]
The authors of this paper previously developed the explicit form of Newmark-β method to identify moving loads on an intact bridge structure [32]
The error of the reconstructed strain at mid-span is increased with an increase in speed; According to Figures 20 and 21, element six is detected as a damaged element and its extension is quantified reasonably at all sampling frequencies and vehicle speeds, there are large false positives at other elements due to measurement noise and modeling errors of the boundary conditions; According to Figures 22 and 23, both the front and rear identified loads are fluctuating around the static axle values (22 N), and the identified resultant load is fluctuating around the total static weight of the vehicle (44 N), showing the accuracy of the method
Summary
Moving load-based damage detection methods have attracted significant attention recently, as they have several advantages over other damage detection methods, such as [1,2,3,4,5,6,7,8,9,10]:. The study applied a Bayesian inference-based regularization approach in an attempt to solve the ill-posed least squares problem for the unknown vehicle axle loads While this method was numerically verified over different vehicle speeds and levels of noise, both for a supported bridge and a three-span continuous bridge, the effect of roughness was not directly considered, and the method showed load identification errors over mid supports. The authors of this paper developed the method for moving load identification for bridge structures and verified the results numerically and experimentally, which are published in the reference [32] This project is believed to be among the few studies on simultaneous identification of moving load and structural damage considering factors such as measurement noise, road surface roughness, sampling frequency, sensor placement, number of spans, number of elements in the finite element model of the bridge, and the vehicle speed.
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