Abstract
An aged bridge’s performance is affected by degradation and becomes one of the major concerns in maintenance. A preliminary, simple and workable procedure of bridge damage detection is required to minimize maintenance costs. In the past, frequency is one of the most common indicators to detect damage occurrence. Recent research found that using frequency as a health indicator still has room to improve. Alternatively, dynamic displacement is used as an indicator in the current study. These dynamic displacements are reconstructed based on measured acceleration records from micro electro mechanical system (MEMS) sensors. The Newmark-beta method with Windows is proposed to acquire the reconstructed displacements of considered bridges. To demonstrate the accuracy and applicability of the proposed approach, three different experiments are carried out; (i) A small scale bridge with the implementation of MEMS acceleration sensors; (ii) a numerical complex finite element method (FEM) bridge model; (iii) an actual bridge with the implementation of MEMS acceleration sensors and narrow bandwidth Internet of things (NB-IoT) technology. The first experiment shows that the proposed method can successfully identify the difference between damaged/undamaged bridges and determine damage location. The second experiment indicates that the proposed method is able to identify the difference between stiffened/unstiffened bridges. The last experiment shows the applicability of the proposed method on an actual bridge health monitoring project.
Highlights
Over a decade, many researchers have attempted to address the issue of bridge health monitoring, in which one of the main focuses is to deliver a simple and sustainable monitoring procedure
Both micro electro mechanical system (MEMS) accelerometers and linear variable differential transformers (LVDT) are used for measurement, This in subsection introduces the results of the 1 detection using reconstructed displacements as shown
This study introduces a novel approach for bridge health monitoring utilizing a displacement This study introduces a novel approach for bridge health monitoring utilizing a displacement
Summary
In addition to utilizing ANN in the approach of a model update-based method, Li et al [9] adopted an optimization technique to enhance the accuracy of the simulated model In their optimization task, the objective is to minimize the difference between the extracted features from the actual and simulated bridges. Huseynov et al [12] adopted a gravity sensor to measure the tilt angle to differentiate the damaged and original bridges Another innovative featured-based approach is proposed by Zhang et al [13]; the damage index was calculated through time series analysis combining autoregressive with the exogenous prediction model. Noise resistance or sensitivity to different loading conditions is worth investigation; it is beyond the scope of the current study
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