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Distributed Fiber Optic Sensor-Based Strain Monitoring of a Riveted Bridge Joint Under Fatigue Loading | IEEE Journals & Magazine | IEEE Xplore

Distributed Fiber Optic Sensor-Based Strain Monitoring of a Riveted Bridge Joint Under Fatigue Loading


Abstract:

Riveted steel bridges, which were built in the early 20th century, require their regular structural integrity assessment to avoid any catastrophic failure. This article p...Show More

Abstract:

Riveted steel bridges, which were built in the early 20th century, require their regular structural integrity assessment to avoid any catastrophic failure. This article presents continuous strain monitoring of a single riveted lap joint, which is a representative critical element of riveted steel bridges through an optical frequency domain reflectometry (OFDR)-based distributed fiber optic sensor (DFOS). The aim of this study was to instrument a DFOS on a single riveted lap joint for monitoring the surface and critical strains experienced by the rivet joint under two fatigue loading conditions and also to compare the strain transfer between the two commonly used adhesives for bonding the DFOS. Initially, through finite element analysis (FEA), a location for installing the DFOS was identified, and also a strategy was developed for monitoring the critical location of the joint during fatigue loading. Subsequently, the DFOS was instrumented on the riveted joint at the identified location in two segments, where similar strain levels were expected with the aid of two types of adhesives: cyanoacrylate and epoxy. The strains on the rivet joint were monitored under high cycle fatigue (HCF) for up to 2\times {10}^{6} loading cycles with constant stress amplitude and followed by low cycle fatigue (LCF) loading with increasing stress amplitude until the failure of the specimen. The results showed that the DFOS could continuously sense the cyclic peak strain of - 223\,\,\mu \varepsilon under HCF conditions and a peak maximum strain of - 1244\,\,\mu \varepsilon under LCF conditions. Furthermore, the internal critical strain on the rivet joint during loading could be monitored with the application of the developed damage monitoring strategy and DFOS strain data. Finally, the DFOS segment bonded using cyanoacrylate measured marginally high strains than epoxy adhesive during the HCF test.
Article Sequence Number: 6009610
Date of Publication: 30 July 2021

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I. Introduction

Before the advent of high-strength bolts, rivets were the primary fasteners used in the construction of steel bridges. Steel bridges that were constructed during the early 20th century continue to be in operation. Replacements of such steel structures that have surpassed their intended service life may not be feasible because of the economic challenges involved in replacing these structures [1]. To ensure safe and reliable operation, it is paramount that the occurrence of damage in such infrastructures is well followed and controlled, enabling a fast action of condition screening that can minimize the adverse effects and inherent repair costs [2]. Structural health monitoring (SHM) offers the potential for these structures to be monitored for the occurrence of damage. In this context, SHM can be defined as advanced nondestructive testing (NDT) technology to ensure the integrity of structures during their operational life [3]. In the basic form, the SHM process is taking continuous or regular measurements and analysis of those collected parameters for the purpose of warning about the potential failures or accidents [4].

Cites in Papers - |

Cites in Papers - IEEE (3)

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1.
R.Zh. Aimagambetova, A.D. Mekhtiyev, O.V. Stukach, "Experimental Studies of Laboratory Samples of Fiber-Optic Sensors within Reinforced Concrete Building Construction. Part 1: Overview", 2024 International Seminar on Electron Devices Design and Production (SED), pp.1-9, 2024.
2.
Caiyun Li, Hongkun Zheng, Lingmei Ma, Chen Zhu, Yiyang Zhuang, Wei Peng, Jianguo Wang, Weiwang Hu, Yun-Jiang Rao, "Optimizing the Demodulation Method for DAS System Based on Point-Backscattering-Enhanced Fiber", IEEE Transactions on Instrumentation and Measurement, vol.73, pp.1-8, 2024.
3.
Kuan Ju, Shuo Weng, Chun Li, Lihui Zhao, Bo Li, Yue Hu, Yang Gao, Fuzhen Xuan, "Random Load Pattern Recognition of Test Road Based on a Laser Direct Writing Carbon-Based Strain Sensor and a Deep Neural Network", IEEE Transactions on Instrumentation and Measurement, vol.72, pp.1-9, 2023.

Cites in Papers - Other Publishers (4)

1.
Tengjiao Jiang, Gunnstein T. Frøseth, Anders Rønnquist, Xuan Kong, Lu Deng, "A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network", Computer-Aided Civil and Infrastructure Engineering, 2024.
2.
Yelena Neshina, Ali Mekhtiyev, Valeriy Kalytka, Nurbol Kaliaskarov, Olga Galtseva, Ilyas Kazambayev, "Fiber-Optic System for Monitoring Pit Collapse Prevention", Applied Sciences, vol.14, no.11, pp.4678, 2024.
3.
Tengjiao Jiang, Gunnstein Thomas Frøseth, Anders Rønnquist, "A robust bridge rivet identification method using deep learning and computer vision", Engineering Structures, vol.283, pp.115809, 2023.
4.
Hasan Borke Birgin, Antonella D’Alessandro, Maurizio Favaro, Cesare Sangiorgi, Simon Laflamme, Filippo Ubertini, "Field investigation of novel self-sensing asphalt pavement for weigh-in-motion sensing", Smart Materials and Structures, vol.31, no.8, pp.085004, 2022.
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References

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