A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change

Corbally, Robert; Malekjafarian, Abdollah

doi:10.1016/j.engstruct.2021.113783

Cited by 64 publications

(25 citation statements)

References 40 publications

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“…Many studies have estimated natural frequencies from acceleration responses obtained using numerical models [ 15 ] as well as bridge field measurements in a form of free vibration [ 16 ], forced vibration [ 14 , 17 ], or ambient vibration [ 18 ]. The natural frequencies are considered as some of the key damage detection features for these studies and have provided reasonable results for the detection of both local [ 19 , 20 ] and global damage [ 11 , 21 , 22 , 23 ]. Although, natural frequencies are not considered to be ideal, because of their low-sensitivity to damage [ 3 , 24 ], changes in the natural frequencies and modal properties have been used to detect scour damage in multiple span structures [ 5 , 11 , 25 , 26 ] and longer span bridges [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Dhakal and Malla [ 39 ] employ indirect measurements to estimate railway bridge natural frequencies in an experimental setup and compare their results with a finite element (FE) model of the bridge. Studies on extracting bridge frequencies from drive-by measurements have used many numerical techniques: Hilbert Transform (HT) [ 40 ], moving average filtration [ 23 , 33 ], wavelet analysis [ 11 ], signal decomposition techniques [ 41 ], and frequency-independent underdamped pinning stochastic resonance [ 36 ]. Although these techniques have shown effective results in the numerical studies, and some single span real bridges, their implementation on multiple span bridges still requires more testing and research.…”

Section: Introductionmentioning

confidence: 99%

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Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

Malekjafarian

Khan

O’Brien

et al. 2022

Sensors

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In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. The bridge includes a series of simply supported spans, two of which collapsed in 2009 and were replaced. The replaced spans are stiffer than most of the others and these differences resulted in higher natural frequencies. An indirect bridge monitoring approach is employed in which acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct showing the locations of the two replaced spans with higher stiffness. For the indirect approach, an Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span. This is carried out by analysing the Instantaneous Frequencies (IFs) from the calculated intrinsic mode functions. The average of the IFs calculated using 41 runs of the instrumented train (with varying carriage mass and speed for each run) are used to estimate the natural frequencies. To assess the feasibility of the indirect approach, a bespoke set of direct measurements was taken using accelerometers attached successively on each span of the viaduct. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches. In addition, the instantaneous amplitudes of the drive-by signals are used to indicate the location of the stiffer spans. Finally, the accuracy and robustness of the indirect approaches for monitoring of multi span bridges are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

Malekjafarian

Khan

O’Brien

et al. 2022

Sensors

Self Cite

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“…However, the variation of mechanical performance at different structural members’ cross-sections is not the same in the limit loading-capacity state. This situation appears to be a typical problem in most such applications, so it is not reasonable to evaluate the structural performance by the method; Existing studies of the data-driven prediction methods [ 25 ] have relied on substantial sample data and repeated iterative calculations. Researchers must adjust the proportion between the sample data, the total sample, and other calculation parameters to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies of the data-driven prediction methods [ 25 ] have relied on substantial sample data and repeated iterative calculations. Researchers must adjust the proportion between the sample data, the total sample, and other calculation parameters to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is developed, including the ultimate loading-capacity modeling of CFST members, multi-parameter selection, ultimate thresholds presetting based on the finite element method, data processing, crucial parameters determination among sub-parameters, multi-parameter regression, ultimate state prediction, and system maintenance decision-making suggestions based on the multi-parameter performance evaluation. A degenerated ultimate loading-capacity model of CFST members is adopted in the finite element analysis and multi-parameter performance assessment. The multi-source heterogeneous data processing and temperature-effect elimination are performed for the data processing. The key sub-parameters were determined by the Principal Component Analysis method and the Entropy-weight method. The polynomial mathematical model is used in the multi-parameter regression, and the ±95% confidence bounds were verified. The system maintenance decision-making model combines the relative monitoring state, the relative ultimate state by the numerical analysis, and the relative residual life of degenerated members. The optimal system maintenance decision-making suggestions for the bridge maintenance system can be identified, including the most unfavorable maintenance time and parameter index. A case study on a CFST truss-arch bridge is conducted to the proposed algorithms. The obtained results demonstrated that the crack width deserves special attention in concrete bridge maintenance. Additionally, these technologies have enormous potential for the life-cycle performance assessment of the structural health monitoring system for existing concrete bridge structures.

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“…The reported methods mainly rely on advanced signal processing techniques and machine learning methods for damage detection. For instance, [11,12] used data driven techniques and statistical analysis for damage detection and quantification. While [13,14] estimate the contact point response between vehicle and bridge for damage detection and localisation.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

Sarwar¹,

Cantero²

2022

SSRN Journal

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A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change

Cited by 64 publications

References 40 publications

Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

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

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

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