Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

Zhan, Jiawang; Wang, Chuang; Fang, Zhiheng

doi:10.3390/su13031474

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…Xu et al employed a multiscale FE model with a multi-type sensor placement approach for damage detection of loosening bolts on a large transmission tower [179]. Zhan et al utilized a two-step identification approach involving a PNN algorithm and an FE model updating technique for joint damage identification in a steel bridge [180]. Yu et al proposed a SDD method at the joint of a bridge with bolted connections, utilizing a fuzzy clustering algorithm and measured FRF data.…”

Section: Model Updating-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: Model Updating-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

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“…The total number of joints in the jacket structure of the Gageocho ORS is 36, as shown in Figure 5. There was a recent study wherein damage to the lattice structure of the joints was modeled in a manner that reduced the elasticity modulus as well as the elements attached to them [38]. Hence, in this study, damage was also considered as a decrease in the modulus of elasticity as well as in the elements included in a joint, and the damage level is defined by Equation (11).…”

Section: Damage Scenariosmentioning

confidence: 99%

Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure

Kim

Oh²,

Min³

2022

Sensors

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The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.

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“…Substituting covariance matrix Γ from Equation (12), eigenvalue matrix Λ from Equation (22) and eigenvector matrix Ψ from Equation (30) into Equation (10), the coefficient ψ 11 can be obtained to satisfy the following relationship:…”

Section: Theoretical Proof Of the Effectiveness Of The Directional Pr...mentioning

confidence: 99%

“…According to the model category, baseline models can be divided into finite element (FE) or data baseline models. FE baseline models [10][11][12] require a complex finite element model (FEM) of a bridge and then perform model updating through many iterative operations to obtain the real modal parameters of the bridge. This process is very time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

Zhang

Wang²,

2022

Sustainability

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In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification.

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Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

Cited by 7 publications

References 28 publications

A comprehensive review on health monitoring of joints in steel structures

A comprehensive review on health monitoring of joints in steel structures

Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure

Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

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