Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines

Zhang, Shaoyi; Liu, Yang

doi:10.3390/app9091805

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…Static influence line deflection [15][16][17] Dynamic properties are not needed. The influencing factors are loading, stiffness, and mass of bridges.…”

Section: Methods Descriptionsmentioning

confidence: 99%

Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

et al. 2020

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An aged bridge’s performance is affected by degradation and becomes one of the major concerns in maintenance. A preliminary, simple and workable procedure of bridge damage detection is required to minimize maintenance costs. In the past, frequency is one of the most common indicators to detect damage occurrence. Recent research found that using frequency as a health indicator still has room to improve. Alternatively, dynamic displacement is used as an indicator in the current study. These dynamic displacements are reconstructed based on measured acceleration records from micro electro mechanical system (MEMS) sensors. The Newmark-beta method with Windows is proposed to acquire the reconstructed displacements of considered bridges. To demonstrate the accuracy and applicability of the proposed approach, three different experiments are carried out; (i) A small scale bridge with the implementation of MEMS acceleration sensors; (ii) a numerical complex finite element method (FEM) bridge model; (iii) an actual bridge with the implementation of MEMS acceleration sensors and narrow bandwidth Internet of things (NB-IoT) technology. The first experiment shows that the proposed method can successfully identify the difference between damaged/undamaged bridges and determine damage location. The second experiment indicates that the proposed method is able to identify the difference between stiffened/unstiffened bridges. The last experiment shows the applicability of the proposed method on an actual bridge health monitoring project.

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“…Static influence line deflection [15][16][17] Dynamic properties are not needed. The influencing factors are loading, stiffness, and mass of bridges.…”

Section: Methods Descriptionsmentioning

confidence: 99%

Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

et al. 2020

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“…These approaches can be broadly classified based on whether the load-response behaviour being analysed can be described using either statics, which depends only on structural stiffness, or dynamics, where the effects of inertia and damping must also be considered. Static responses obtained from strain [5], [6] can identify damage close to the sensor, whereas static response captured via displacement [7]- [9] theoretically will be affected by damage anywhere in the deck, albeit displacement is typically harder to measure on site. Bridge rotation response is also affected by damage both near and far from the sensor but it is easier to measure than displacement.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach to Filter Specification for Measuring Quasi-Static Bridge Rotation Under Moving Loads Using DC Accelerometers

Ferguson,

Hester,

Woods

2024

IEEE Access

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Low-cost, DC micro-electromechanical systems (MEMS) accelerometers can be employed to provide a mechanism to capture the relatively small levels of rotation typically seen on some bridges allowing their structural response to be measured. The effect of ambient vehicle traffic loads crossing the bridge can offer a cost-effective, less disruptive alternative method for continuous bridge structural health monitoring (BSHM). However, utilising moving traffic loads to assess the quasi-static structural behaviour is more challenging as the measured sensor data contains additional signal components due to the dynamics of the vehicle-bridge interaction system. To overcome this challenge, this paper presents a simplified approach for the specification of an appropriate filtering scheme to extract the desired quasi-static bridge response based on only the bridge's geometry and the vehicle speed. The originality of the method proposed herein lies in its wide applicability and extensibility due to its basis on the widely accepted principles of bridge influence lines. For both BSHM researchers and practitioners alike, the significance of the proposed method lies in its systematic approach, thus helping to unlock the potential for long-term monitoring of bridge quasi-static response under ambient traffic loading. The approach is demonstrated through its successful application in a field trial on an in-service highway bridge.INDEX TERMS finite impulse response filters, bridge structural health monitoring, quasi-static bridge rotation extraction, DC MEMS accelerometers, bridge influence lines, practical bridge rotation measurements.

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“…On the other hand, the road roughness of the bridge deck is an inherent physical phenomenon present in almost all bridges, due to construction quality and maintenance capacity. Several scholars have observed that road roughness amplifies the dynamic response due to the moving action of the mass, potentially compromising the safety and serviceability of the structure [23,24]. Existing studies exhibit certain deficiencies, primarily in the assumption that vehicles on the bridge are typically regarded as one or several separate moving load models.…”

Section: Introductionmentioning

confidence: 99%

Coupled Vibration of a Vehicle Group–Bridge System and Its Application in the Optimal Strategy for Bridge Health Monitoring

Yan,

Zhao,

2024

Applied Sciences

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The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges.

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Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines

Cited by 13 publications

References 32 publications

Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

A Systematic Approach to Filter Specification for Measuring Quasi-Static Bridge Rotation Under Moving Loads Using DC Accelerometers

Coupled Vibration of a Vehicle Group–Bridge System and Its Application in the Optimal Strategy for Bridge Health Monitoring

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