Acoustic emission monitoring of strengthened steel bridges: Inferring the mechanical behavior of post-installed shear connectors

Ebrahimkhanlou, Arvin; Salamone, Salvatore; Ebrahimkhanlou, Arash; Azad, Amir Reza Ghiami; Kreitman, Kerry; Helwig, Todd A.; Williamson, Eric B.; Engelhardt, Michael D.

doi:10.1117/12.2514231

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…AE data were analyzed using both time-driven and hit-driven features extracted from AE. In [46], an AE monitoring approach was proposed for old steel bridges strengthened with post installed shear connectors. The proposed method leverages the difference in AEs from shear connectors before and after they engage in shear transfer.…”

Section: Related Workmentioning

confidence: 99%

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Habib

Rai

Kim

2020

Sensors

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Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams.

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Section: Related Workmentioning

confidence: 99%

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Habib

Rai

Kim

2020

Sensors

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“…Analyzing the acoustic emissions of post-installed shear connectors proved to be an effective method of estimating the remaining life of the shear connectors. 17 Azad and Mafipour 18 developed a method for the analysis of shear connectors with arbitrary distributions. Considering the nonlinear behavior of shear connectors and partially composite beams sustaining moving loads, the recommended method was an appropriate method for fatigue assessment and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Using a novel acoustic emission monitoring approach, a study was conducted on the remaining life of post‐installed shear connectors. Analyzing the acoustic emissions of post‐installed shear connectors proved to be an effective method of estimating the remaining life of the shear connectors 17 . Azad and Mafipour 18 developed a method for the analysis of shear connectors with arbitrary distributions.…”

Section: Introductionmentioning

confidence: 99%

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

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Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors.

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“…Developing a simple economical, flexible and at the same time accurate sensor is very much in demand. Sensor types developed for SHM purposes in recent decades include electric strain gages, piezoelectric sensors, cement-based strain gauge, corrosion sensors, nano material-based sensors, wireless sensors, accelerometers, inclinometers, acoustic emission sensors [11], wave propagation sensing devices and various fiber optic sensors (FOS). Additionally, advanced sensor and sensing technologies, such as fiber optics and Bragg grating sensors [12,13] and those based on Global Positioning System [14] have been recently developed for strain, displacement, and other response measurement.…”

Section: Introductionmentioning

confidence: 99%

Novel Approaches for Fracture Detection in Steel Girder Bridges

Abedin

Mehrabi

2019

Infrastructures

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The bottom flanges of steel plate girder bridges can be considered fracture-critical elements depending on the number of girders and bridge configuration. For such cases, it is required that inspection of these bridges be carried out using costly “arms-length” approach. New techniques in structural health monitoring (SHM) that use non-contact sensors and self-powered wireless sensors present alternative approach for inspection. Application of such techniques would allow timely detection and application of repair and strengthening, in other word, providing for more resilient bridges. This paper investigates the feasibility of using a handful of self-powered wireless or non-contact sensors for continuous or periodic monitoring and detection of fracture in steel plate girder bridges. To validate this concept, vibration measurements were performed on an actual bridge in the field, and detailed finite element analyses were carried out on a multi-girder bridge. The records obtained show that vibration amplitude was significantly increased for fractured girder, and a distinct pattern of strain variation was registered in the vicinity of fracture, all of which can be detected effectively with relevant sensors. Moreover, the amplitude and frequency of the vibration was shown to be significant enough for providing the required power for typical sensor(s).

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Acoustic emission monitoring of strengthened steel bridges: Inferring the mechanical behavior of post-installed shear connectors

Cited by 7 publications

References 16 publications

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Novel Approaches for Fracture Detection in Steel Girder Bridges

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