Vibration-Based Damage Identification in Steel Girder Bridges Using Artificial Neural Network Under Noisy Conditions

Nick, Hooman; Aziminejad, Armin

doi:10.1007/s10921-020-00744-8

Cited by 18 publications

(7 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For steel beam units, they used damage indices based on MSE in ANNs, and for bridge decks, they used a loss index based on modal flexibility in ANNs. Nick et al [167] studied the anti-noise performance of three SDI methods based on MSE, modal flexibility, and modal curvature. They then trained an In vibration-based SDI, the change of structural modal characteristics is an important damage index.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

View full text Add to dashboard Cite

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In this work, we investigate the feasibility of a GSP-based SHM approach for damage detection of bridges. Aging bridge infrastructures worldwide pose growing challenges due to increased mobility, traffic volume, and climate change, which accelerate their deterioration [5], [6]. Current procedures for bridge maintenance primarily depend on manual and visual inspections, which are costly, time-consuming and largely subjective.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Cheema,

Sarwar,

Gogineni

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Structural health monitoring (SHM) of bridges is crucial for ensuring safety and long-term durability, however, standard damage-detection algorithms are computationally intensive. This paper proposes a computationally-efficient algorithm based on graph signal processing (GSP) to leverage the underlying network structure in the data. Under the assumption that damages impact both spatial and temporal structures of the sensors data, the algorithm combines spatial and temporal information from accelerometers by computing the smoothness of graph signals expanded along time. The Kullback-Leibler (KL) divergence is used as dissimilarity metric to distinguish between healthy condition and presence of a damage, while Tukey's method for outliers removal and sequential detection via exponential Weighted moving average (EWMA) are then employed for performance improvement. The proposed GSP-based SHM system is appealing in terms of simplicity and low-complexity, and is also suitable for realtime monitoring. The effectiveness in terms of detection performance is validated both on synthetically-generated data and real-world measurements.

show abstract

“…The modal curvature utility information entropy index is provided which is based on the information entropy theory and combines the modal curvature index in beam structure damage diagnosis [14]. The vibration-based technique is utilized with damage prediction methods such as strain energy damage index, model flexibility, and model curvature to identify the damage in structure [15]. The initial three bending modes are used to find out damage indices of finite element model of the I-40 bridge [16].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of crack locations in beam using artificial neural network‐based modified curvature damage index

Gupta

Das

2023

Materialwissenschaft Werkst

View full text Add to dashboard Cite

Although the frequency response‐curvature methodology is commonly used to detect irregularities in mechanical and civil structures, the artificial neural network‐based frequency response‐curvature damage index method may have good efficacy in the detection and localization of structural damages. By utilizing experimental data sets, a novel method is proposed to pinpoint a saw‐cut damage location and the degree of damage in beam models. Using a dynamic data logger, the frequency response function of a beam model is obtained for altering damage levels at different positions. As frequency response data contains environmental and operational noise, the accuracy of obtained results may get reduced. To improve the accuracy by reducing the noise effect, the experimentally obtained frequency response data is trained through an artificial neural network. Using central difference approximation, the sets of trained modal data are utilized to determine the improved mode shape curvature. The curvature damage index is then obtained by using the improved mode shape curvature for different damaged scenarios to ultimately identify structural damages.

show abstract

Vibration-Based Damage Identification in Steel Girder Bridges Using Artificial Neural Network Under Noisy Conditions

Cited by 18 publications

References 73 publications

The Current Development of Structural Health Monitoring for Bridges: A Review

The Current Development of Structural Health Monitoring for Bridges: A Review

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Evaluation of crack locations in beam using artificial neural network‐based modified curvature damage index

Contact Info

Product

Resources

About