Present day status and numerical modelling of heritage masonry bridges of Kalka-Shimla Mountain Railways

Shimpi, Vinay; Sivasubramanian, Madappa V. R.; Singh, S.B.

doi:10.1504/ijmri.2022.122516

Cited by 3 publications

(2 citation statements)

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“…The monitoring approaches that are usually applied to masonry constructions are both vibration- and strain-based SHM methods; the monitoring of displacements is also widely adopted in common practice, especially to assess crack growth over time [ 11 , 12 ]. The potential applications of vibration-based SHM methods encompass the monitoring of slender structures, such as historic masonry towers [ 13 , 14 , 15 ] and buildings, such as palaces and churches [ 16 , 17 , 18 ], and bridges [ 19 , 20 , 21 ]. The practical applications of strain-based SHM methods to similar structural settings can be found in the literature [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

Meoni

D’Alessandro

Saviano

et al. 2023

Sensors

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A diffuse and continuous monitoring of the in-service structural response of buildings can allow for the early identification of the formation of cracks and collapse mechanisms before the occurrence of severe consequences. In the case of existing masonry constructions, the implementation of tailored Structural Health Monitoring (SHM) systems appears quite significant, given their well-known susceptibility to brittle failures. Recently, a new sensing technology based on smart bricks, i.e., piezoresistive brick-like sensors, was proposed in the literature for the SHM of masonry constructions. Smart bricks can be integrated within masonry to monitor strain and detect cracks. At present, the effectiveness of smart bricks has been proven in different structural settings. This paper contributes to the research by investigating the strain-sensitivity of smart bricks of standard dimensions when inserted in masonry walls subjected to in-plane shear loading. Real-scale masonry walls instrumented with smart bricks and displacement sensors were tested under diagonal compression, and numerical simulations were conducted to interpret the experimental results. At peak condition, numerical models provided comparable strain values to those of smart bricks, i.e., approximately equal to 10−4, with similar trends. Overall, the effectiveness of smart bricks in strain monitoring and crack detection is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

Meoni

D’Alessandro

Saviano

et al. 2023

Sensors

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show abstract

“…Compared with time domains, analyses in the frequency domain (FD) do not require the calculation of modalities and are generally faster and simpler for extracting modal parameters. Frequency-spatial domain decomposition (FSDD) is the state-of-art FD technique that can directly identify modal frequencies and damping in the FD based on enhanced power spectral density (PSD) [20], thereby avoiding the beat frequency phenomenon [21] and obtaining accurate identification results [15].…”

Section: Introductionmentioning

confidence: 99%

Integrated fault diagnosis of rolling bearings based on improved multichannel singular spectrum analysis and frequency–spatial domain decomposition

Sun¹,

Sun²,

Wang³

2022

Meas. Sci. Technol.

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Extensive fault information can be obtained from the vibration signals of rotating machines with faulty rolling bearings. However, the diagnosis of compound faults is challenging because of their easy mix-ups, which can lead to faulty diagnosis and judgment. This study improves the multichannel singular spectrum analysis (MSSA) by using convex optimization. In addition, an integrated fault diagnosis technology for rolling bearings using an improved MSSA and frequency–spatial domain decomposition (FSDD) was developed. This approach involves two primary stages: signal preprocessing and fault diagnosis. The proposed method was tested to diagnose faults in the rolling bearings of pellet mills. Signal preprocessing can significantly improve the quality of a vibration signal and preserve modal information that characterizes a fault. Fault diagnosis identifies the modal parameters entirely and accurately from the reconstructed vibration signal, and determines the degree of damage. The proposed method can aid in the robust diagnosis of faulty rolling bearings under severe operating conditions.

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A comprehensive survey of masonry bridge assessment methods: past to present

Polepally,

Pasupuleti,

Kalapatapu

2024

Innov. Infrastruct. Solut.

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Present day status and numerical modelling of heritage masonry bridges of Kalka-Shimla Mountain Railways

Cited by 3 publications

References 0 publications

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

Integrated fault diagnosis of rolling bearings based on improved multichannel singular spectrum analysis and frequency–spatial domain decomposition

A comprehensive survey of masonry bridge assessment methods: past to present

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