Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks

Nguyen, Van Vu; Dackermann, Ulrike; Li, J.; Alamdari, Mehrisadat Makki; Mustapha, Samir; Runcie, Peter; Ye, L.

doi:10.56748/ejse.141921

Cited by 18 publications

(5 citation statements)

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“…Furthermore, to tackle the challenges of incomplete and imprecise data, Mehrjoo et al [269] discussed the numerical analyses of an ANN-based method for the intensity estimation of damage in truss bridge joints. Favarelli et al [270], and Weinsteinet al [271], Nguyen et al [272], and Jayasundara [273], employed ANN-based anomaly detection with bridge vibrational data, and Xu et al [274] developed an ANN-based two-step algorithm for vibration-based damage identification (VBDI) applied on the Crowchild Bridge located in Alberta, Canada. Eftekhar et al [275] integrated ANNs with proper orthogonal decomposition (POD) and subspace-based damage indicators, respectively.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Eftekhar et al [275] integrated ANNs with proper orthogonal decomposition (POD) and subspace-based damage indicators, respectively. Nguyen et al [276] leveraged ANNs with residual FRFs and PCA for SHM of a concrete arch beam replica. Tran [277] also provided a thesis focused on the damage detection accuracy of ANN-based methods.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Operational Modal Analysis on Bridges: A Comprehensive Review

Hasani,

Freddi

2023

Infrastructures

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Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Operational Modal Analysis on Bridges: A Comprehensive Review

Hasani,

Freddi

2023

Infrastructures

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“…In recent years, several authors (e.g., Jin et. al 2016, Onur and Abdeljaber 2015, Nguyen et. al 2015 have concluded that structural damage detection is a problem of pattern recognition, in which a classification is made as function of physical properties of a system.…”

Section: Introductionmentioning

confidence: 98%

Potential of Neural Networks for Structural Damage Localization

Abambres,

2024

Preprint

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Fabrication technology and structural engineering states-of-art have led to a growing use of slender structures, making them more susceptible to static and dynamic actions that may lead to some sort of damage. In this context, regular inspections and evaluations are necessary to detect and predict structural damage and establish maintenance actions able to guarantee structural safety and durability with minimal cost. However, these procedures are traditionally quite time-consuming and costly, and techniques allowing a more effective damage detection are necessary. This paper assesses the potential of Artificial Neural Network (ANN) models in the prediction of damage localization in structural members, as function of their dynamic properties – the three first natural frequencies are used. Based on 64 numerical examples from damaged (mostly) and undamaged steel channel beams, an ANN-based analytical model is proposed as a highly accurate and efficient damage localization estimator. The proposed model yielded maximum errors of 0.2 and 0.7 % concerning 64 numerical and 3 experimental data points, respectively. Due to the high-quality of results, authors’ next step is the application of similar approaches to entire structures, based on much larger datasets.

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“…utilized the PINN to effectively predict the water flow in unsaturated soils governed by the intricate Richardson‐Richards equation. All these studies highlight the potential of PINN in solving pile‐related emerging concerns such as damage detection, 32,33 reliability assessment, 9,10 and design optimization 11–13 . Even though preliminary investigation of PINN for pile assessment has been conducted, 34 it does not comprehensively capture all the crucial factor for pile response, such as geometric nonlinearity, impeding its application.…”

Section: Introductionmentioning

confidence: 99%

Physics‐informed neural networks for large deflection analysis of slender piles incorporating non‐differentiable soil‐structure interaction

Ouyang,

Li,

Chen

et al. 2024

Num Anal Meth Geomechanics

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Physics‐informed neural networks (PINN) is an emerging machine learning technique and has been applied in different areas successfully. To benefit pile analysis from this innovative technique, this paper addresses several problems that arise when extending PINN to the large deflection analysis of slender piles accounting for nonlinear Soil‐Structure Interaction (SSI). The governing equations for the structural behavior of piles, considering geometric nonlinearity, are elaborated at first, based on which a PINN framework is constructed correspondingly with a model training process. A series of normalization factors are introduced to the loss function to enhance model training stability. Additionally, a regression‐based soil resistance estimation is developed to prevent non‐convergence and instability that may occur during the model training when encountering non‐differentiable SSI. Extensive examples are provided to validate the robustness and accuracy of the proposed analysis method for piles under complex geological conditions. Furthermore, several case studies are conducted, revealing the necessity of appropriate loss normalization and the effectiveness of regression‐based estimation for reflecting non‐differentiable functions in the PINN study.

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Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks

Cited by 18 publications

References 0 publications

Operational Modal Analysis on Bridges: A Comprehensive Review

Operational Modal Analysis on Bridges: A Comprehensive Review

Potential of Neural Networks for Structural Damage Localization

Physics‐informed neural networks for large deflection analysis of slender piles incorporating non‐differentiable soil‐structure interaction

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