Combined Joint and Member Damage Identification of Semi-Rigid Frames with Slender Beams Considering Shear Deformation

Xiao, Feng; Meng, Xiuxia; Zhu, Wei; Chen, Gang S.; Yu, Youcheng

doi:10.3390/buildings13071631

Cited by 5 publications

(4 citation statements)

References 50 publications

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“…Equation (2b) theoretically affirms that the resultant EMA response inherently encapsulates the mechanical properties of the host structure (m, c, k). When the host anchor-age undergoes damage, such as fatigue cracks or corrosion (indicating stiffness or mass changes [46,47]) in the anchorage, the EMA response undergoes variations reflective of the altered mechanical properties. Monitoring such EMA changes facilitates the identification of structural damage in the anchorage.…”

Section: The Fundamentals Of Ema Responsementioning

confidence: 99%

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Ho,

Kim,

Hoang

et al. 2024

Buildings

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Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages.

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Section: The Fundamentals Of Ema Responsementioning

confidence: 99%

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Ho,

Kim,

Hoang

et al. 2024

Buildings

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“…From the perspective of data statistics, the location of the strain energy of mutation is the location where the damage occurs. Xiao et al [102] used the static flexibility method to identify damage in semi-rigid frames with slender beams. The proposed method could successfully identify damage to structural nodes as well as damage to combinations of the nodes and components of the structure.…”

Section: Assessing Damage Using Static Flexibilitymentioning

confidence: 99%

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Peng,

Yang,

Qin

et al. 2023

Coatings

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Material damage in structures must be detected in a timely manner to prevent engineering accidents. Damage detection based on structural flexibility has attracted widespread attention in recent years due to its simplicity and practicality. This article provides a detailed overview of damage detection methods based on structural flexibility. Depending on the calculation method and data used, flexibility-based methods can be divided into the following categories: flexibility difference, flexibility derivative index, flexibility sensitivity, flexibility decomposition, static flexibility, and combinations of flexibility with other methods. The basic principles and main calculation formulas of various flexibility methods are explained, and their advantages and disadvantages are analyzed. For the method using flexibility difference, the advantage is that the calculation is very simple and does not require the construction of a finite element model of the structure. The disadvantage is that it requires the measurement of modal data of the intact structure, and this method cannot quantitatively assess the degree of damage. For the method using the flexibility derivative index, the advantage is that it only requires the modal data of the damaged structure to locate the damage, but this method is particularly sensitive to noise in the data and is prone to misjudgment. For methods based on flexibility sensitivity and flexibility decomposition, the advantage is that they can simultaneously obtain the location and degree of damage in the structure, but the disadvantage is that they require the establishment of accurate finite element models in advance. Static flexibility methods can compensate for the shortcomings of dynamic flexibility methods, but they usually affect the normal use of the structure during static testing. Combining flexibility-based methods with advanced intelligent algorithms and other methods can further improve their accuracy and efficiency in identifying structural damage. Finally, this article discusses the challenges that have not yet been solved among damage detection methods based on structural flexibility.

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“…Zhu et al, used a combination of statistical regression and deep learning methods to predict the deformation of a dam based on multiple measuring points in different sections of the dam [18]. Xiao et al, proposed a methodology for identifying damage in semi-rigid frames with slender beams, which was applied on semi-rigid frame structures with different cross-sectional shapes by formulating an objective function based on minimizing the difference between the analyzed and measured joint displacements [19]. However, the practical application of these methods to grid structures is often hindered by the large-scale nature of the structures and the significant number of connections and members involved.…”

Section: Introductionmentioning

confidence: 99%

Two-Step Identification Method and Experimental Verification of Weld Damage at Joints in Spatial Grid Structures

Liu,

Huang,

Wang

et al. 2023

Buildings

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Welded joints in grid structures are susceptible to damage and destruction when exposed to random excitation. The complexity of the grid structure poses challenges for realizing the damage recognition of welded joints. In this study, a two-step method is proposed specifically for damage identification of welded joints in grid structures, combining wavelet analysis and fuzzy pattern recognition to accurately identify the location and extent of damage in welded joints. Firstly, the structure is divided based on the analysis of the influence range of joint damage. Key joints are selected within the sub-regions where sensors are installed, and the acceleration response of these key joints is measured. Wavelet analysis is then applied to identify the sub-regions where weld damage occurs. Secondly, an equivalent finite element model is established for joints with varying degrees of damage. The damage index, calculated as the ratio of the absolute value of the difference in the first-order element strain mode of the members, increases with the degree of damage during joint weld damage. By monitoring the changes in the damage index of sensitive members, which exhibit significant changes with varying weld damage degrees, a damage pattern database is constructed for each sub-region. The membership degree between joint damage and the patterns in the pattern database is then calculated to determine the location and degree of weld damage. To validate the effectiveness of the proposed method, an experiment was conducted using a grid structure model with replaceable members. Highly sensitive FBG sensors were designed to measure the acceleration response of the joints, resulting in accurate identification of damaged sub-regions solely through the measurement of key joint acceleration responses. Furthermore, within the damaged sub-regions, the fuzzy pattern recognition method precisely determined the location and degree of weld damage in the joints. The experimental results demonstrate that the proposed method effectively reduces the complexity of the structure by dividing the grid structure into sub-regions, and enables the two-step identification method to achieve successful damage identification for the joints in the grid structure with high efficiency and accuracy.

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Combined Joint and Member Damage Identification of Semi-Rigid Frames with Slender Beams Considering Shear Deformation

Cited by 5 publications

References 50 publications

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Two-Step Identification Method and Experimental Verification of Weld Damage at Joints in Spatial Grid Structures

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