Modal strain energy based enhanced approaches for damage detection and severity estimation

Nguyen, Quy Thue; Livaoğlu, Ramazan

doi:10.1016/j.engfailanal.2023.107142

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Static and dynamic parameter identification methods are two distinct approaches used in the field [ 16 , 17 , 18 , 19 , 20 , 21 ]. Some researchers have made remarkable progress in the field of structural damage identification using static responses.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Xiao,

Yan,

Meng

et al. 2024

Sensors

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Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of parameter identification. Loads can be applied individually to each separate substructure, thereby guaranteeing obvious local static responses. The veracity and efficacy of the proposed methodology are substantiated by applying it to three- and eight-span continuous rigid frame structures. The findings indicate that the proposed approach significantly enhances the efficiency of parameter identification for multispan rigid frames.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Xiao,

Yan,

Meng

et al. 2024

Sensors

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show abstract

“…Broadly, these approaches can be classified into two categories: model-based methods and data-driven methods. Conventional model-based methods focus on extracting dynamic signatures of the monitored structure, such as natural frequency [3], mode shape [4], modal strain energy [5] and modal flexibility [6], which serve as indicators for structural identification. Changes in the structure can be detected by comparing the measured values of these indicators with their theoretical counterparts [7].…”

Section: Introductionmentioning

confidence: 99%

Physics-informed deep learning for structural vibration identification and its application on a benchmark structure

Zhang,

Guo,

Zhang

et al. 2023

Phil. Trans. R. Soc. A.

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Structural vibration identification is an important task in civil engineering that is based on processing measured data from structural monitoring. However, predicting the response at unsensed locations based on limited sensor data can be challenging. Deep learning (DL) methods have shown promise in vibration data feature extraction and generation, but they struggle to capture the underlying physics laws and dynamic equations that govern vibration identification. This paper presents a novel framework called physics-informed deep learning (PIDL) that combines deep generative networks with structural dynamics knowledge to address these challenges. The PIDL framework consists of a data-driven convolutional neural network for structural excitation identification and a physics-informed variational autoencoder for explicit time-domain (ETD) vibration analysis with the generated unit impulse response (UIR) signal of the measured structure. The proposed framework is evaluated on a benchmark structure for structural health monitoring, demonstrating its effectiveness in extracting physics-related dynamics features and accurately identifying excitation signals and latent physics parameters across different damage patterns. Additionally, the incorporation of an ETD method-aided convolution function in the loss function aligns the generated UIR signals with the dynamic properties of the measured structure. Compared with conventional DL-based vibration analysis methods, the PIDL framework offers improved accuracy and reliability by integrating structural dynamics knowledge. This study contributes to the advancement of structural vibration identification and showcases the potential of the PIDL framework in civil structure monitoring applications. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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Hanger replacement and corrosion assessment in a suspension bridge

Sun,

Yao

2023

Structures

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Modal strain energy based enhanced approaches for damage detection and severity estimation

Cited by 5 publications

References 39 publications

Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method

Physics-informed deep learning for structural vibration identification and its application on a benchmark structure

Hanger replacement and corrosion assessment in a suspension bridge

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