A convolutional neural network-based architecture for health monitoring of joint damages in a steel plane frame structure under temperature variability

Naresh, Maloth; Kumar, Vimal; Pal, Joy

doi:10.1007/s42107-023-00895-9

Cited by 1 publication

(1 citation statement)

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“…Classification was achieved by training and testing the image dataset with CNN [249]. Naresh et al proposed a CNN-based approach to identify the weld damage in a single-story plane structure under temperature variability [250].…”

Section: And Dl-based Shm Methods At the Weld Jointmentioning

confidence: 99%

A comprehensive review on health monitoring of joints in steel structures

Naresh,

Kumar,

Pal

et al. 2024

Smart Mater. Struct.

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Steel-framed structures find extensive application in civil engineering projects, including buildings, bridges, and towers, due to their dependable material characteristics, expeditious construction capabilities, and cost-efficiency. In such structures, beams and columns are interconnected through either welding or bolting methods. However, it's imperative to recognize that joints represent the critical areas susceptible to damage stemming from a variety of factors, both human-induced and environmental, in addition to the effects of aging. Over the past few decades, substantial attention has been dedicated to the field of Structural Health Monitoring (SHM) at the joints of steel structures. This study seeks to comprehensively evaluate various methods employed for structural health monitoring at the joints of steel structures, encompassing both bolted and welded connections. While there have been numerous prior review studies that focus on localized and vibration-based techniques for detecting damage at these joints, there is a conspicuous absence of research covering the amalgamation of localized and global approaches across diverse steel structure types. This review paper addresses this gap by offering a thorough examination, incorporating the most recent applications of SHM methodologies employed in research and practical contexts for joint damage detection. Furthermore, it serves as a valuable resource for professionals, engineers, and academics engaged in civil structure design, construction, and maintenance.

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Section: And Dl-based Shm Methods At the Weld Jointmentioning

confidence: 99%