A Decision-Making Algorithm for Concrete-Filled Steel Tubular Arch Bridge Maintenance Based on Structural Health Monitoring

Gui, Chengzhong; Lin, Weiwei; Huang, Zuwei; Xin, Guangtao; Jun, Xiao; Yang, Limin

doi:10.3390/ma15196920

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…The results showed that the hybrid data fusion system had good damage identification performance, anti-noise ability and robustness. Gui et al [188] analyzed the ultimate bearing capacity modeling, limit threshold setting and limit state prediction of concrete-filled steel tubular members. A system maintenance decision model was proposed, which combined the relative monitoring state, the relative final state of numerical analysis and the relative residual life of degraded components.…”

Section: Damage Identification Methods Of Cfst Arch Bridgementioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

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Section: Damage Identification Methods Of Cfst Arch Bridgementioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

Gui,

Duan,

Huang

et al. 2024

Engineering Reports

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The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

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A Decision-Making Algorithm for Concrete-Filled Steel Tubular Arch Bridge Maintenance Based on Structural Health Monitoring

Cited by 2 publications

References 37 publications

A Review on Damage Monitoring and Identification Methods for Arch Bridges

A Review on Damage Monitoring and Identification Methods for Arch Bridges

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

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