Deep learning-based signal processing for evaluating energy dispersal in bridge structures

Ngo-Kieu, Nhi; Nguyen-Da, Thao; Pham-Bao, Toan; Nguyen‐Nhat, Tam; Nguyen‐Xuan, H.

doi:10.1631/jzus.a2000414

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…A predictive model using Machine learning algorithms in the problem of damage identification has been proposed in many studies. Machine learning as neural network pattern recognition (NNPR) serves the damage detection process in beams [11][12][13][14][15] or conditional assessment in bridges [3,16,17]. Besides, machine learning methods are also applied in other structures, such as plates, pipes, and frames [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Pham Bao,

Le-Ngoc,

Vuong Cong

et al. 2023

Frattura Ed Integrità Strutturale

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Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation.

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