Acoustic Emission-based Damage Detection and Classification in Steel Frame Structure Using Wavelet Transform and Random Forest

Biswas, Anupam Kumar; Datta, Asoke Kumar; Topdar, Pijush; Sengupta, Shamik

doi:10.3311/ppci.20447

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…This method employed the wavelet transform and a ML algorithm. The wavelet transform was utilized to extract time-frequency components, which were then used as input for the random forest algorithm to classify damage cases [115]. Li et al proposed a CNN-based technique at the joint of a plate-type structure with a bolted connection using AE data [116].…”

Section: Acoustic Emission-based Shm Methods At the Bolt 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: Acoustic Emission-based Shm Methods At the Bolt Jointmentioning

confidence: 99%

A comprehensive review on health monitoring of joints in steel structures

Naresh,

Kumar,

Pal

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

Automated crack identification in structures using acoustic waveforms and deep learning

Barbosh,

Ge,

Sadhu

2024

J Infrastruct Preserv Resil

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Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature. To automate this process, this paper proposes a deep-learning model to predict the damage severity and its expected location using AE waveforms. The model is based on a densely connected convolutional neural network (CNN) that offers superior feature extraction and minimal training data requirements. Time-domain AE waveforms are used as inputs of the proposed model to automate the process of predicting the severity of damage and identifying the expected location of the damage in structural elements. The proposed approach is validated using AE data collected from a concrete beam and a wooden beam and plate. The results show the capability of the proposed method for predicting the level of damage with an accuracy range of 92-95% and identifying the approximate location of damage with 90-100% accuracy. Thus, the proposed method serves as a robust technique for damage severity prediction and localization in civil structures.

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Acoustic Emission-based Damage Detection and Classification in Steel Frame Structure Using Wavelet Transform and Random Forest

Cited by 2 publications

References 38 publications

A comprehensive review on health monitoring of joints in steel structures

A comprehensive review on health monitoring of joints in steel structures

Automated crack identification in structures using acoustic waveforms and deep learning

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