Drive-by-bridge inspection for damage identification in a cable-stayed bridge: Numerical investigations

Kildashti, Kamyar; Alamdari, Mehrisadat Makki; Kim, C.W.; Gao, Wei; Samali, Bijan

doi:10.1016/j.engstruct.2020.110891

Cited by 47 publications

(9 citation statements)

References 51 publications

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“…McGetrick and Kim [30] applied CWT with different wavelets on the acceleration of vehicle axles to detect damage on the bridge, both numerically and experimentally. Kildashti et al [31] attempted to identify the location and level of cable damage in a cable-stayed bridge by solely analyzing vehicle response measurements. Lei et al [32] suggested the use of discrete wavelet transform analysis on the residual contact response of a moving vehicle to detect bridge damage (cracks) when road roughness is present.…”

Section: Introductionmentioning

confidence: 99%

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Siu,

Filippitzis,

Stoura

et al. 2023

Preprint

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Section: Introductionmentioning

confidence: 99%

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Siu,

Filippitzis,

Stoura

et al. 2023

Preprint

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“…The recovered IMFs by repeated siftings process in EMD made bridge frequencies more visible in the first few IMFs. The IMFs extracted from vehicle response were used as damage indicators of bridge structure (Kildashti et al, 2020; O’Brien et al, 2017). EEMD method introduced by Wu and Huang (2009) to address the mode mixing problem of the EMD was used to identify bridge modal frequencies from vehicle response (Zhu and Malekjafarian, 2019).…”

Section: Introductionmentioning

confidence: 99%

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Zhu

Guo

2022

Advances in Structural Engineering

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Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.

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“…Rahmatalla et al 11 used the finite element (FE) modal analysis combined with the operational vibration waveforms generated by vehicles to guide SHM observers in inspection of old bridges. One of the first attempts to identify cable damage on cable-stayed bridges was presented by Kildashti et al 12 for indirect bridge health monitoring. Soliman et al 13 used non-destructive testing techniques to monitor the crack growth and to support the fatigue life estimation at different periods of service life.…”

Section: Introductionmentioning

confidence: 99%

Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory

et al. 2022

Structural Health Monitoring

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Bridges are critical components of transportation infrastructure. To ensure the long-term performance of bridges and the safety of the public, regular inspections are required during their service. Structural performance assessments are subject to various conditions. Structural deterioration is caused by complex environmental and operational conditions (EOCs) including temperature changes, truckloads, chemical corrosion, etc. In this study, an in-site structural health monitoring (SHM) system is designed and deployed on the Caohekou Bridge in China with a series of sensors installed, providing continuous real-time data for 4 years. Crack width, vertical deformation, concrete strains, temperature, longitudinal displacement and acceleration are monitored and assessed. Time-history monitoring data are comprehensively analysed to advance our understanding of structural deterioration caused by time and temperature. It is very common to lose data during the monitoring process, especially in the long-term run. To overcome the challenge of missing data package and to realize early warning, methods including an SHM systems face, a back propagation neural network and a long short-term memory are proposed to predict the bridge responses under the change of EOCs. It has been proved that the performance of predicted crack widths is close to that of the measured value, and the trend of change is consistent. Such results indicate that approaches proposed that quantitatively assess in-service structure are promising. Therefore, effective and efficient maintenance decisions can be made to ensure an immediate response, long-term safety and serviceability of bridge structures.

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Drive-by-bridge inspection for damage identification in a cable-stayed bridge: Numerical investigations

Cited by 47 publications

References 51 publications

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory

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