Damage identification and bearing capacity evaluation of bridges based on distributed long-gauge strain envelope line under moving vehicle loads

Wu, Bitao; Wu, Gang; Yang, Caiqian; He, Yanling

doi:10.1177/1045389x16629571

Cited by 30 publications

(23 citation statements)

References 15 publications

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“…The damage index proposed in Wu et al [ 26 ] was called macro-strain influence line response envelope (MIE). It consists of the maximum of each macro-strain response obtained by a long-gauge FBG sensor:

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…This is the core part of the damage detection method proposed by Wu et al [ 26 ]. This method uses the MIE of the macro-strain response caused by a vehicle to locate and quantify the damage.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Therefore, this paper conducts a strict comparison on three representative damage detection methods based on long-gauge FBG. The methods employed in this study were proposed by Wu et al [ 26 ], Hong et al [ 27 ] and Chen et al [ 28 ]. For better comparison, these methods’ formats were firstly reformulated and unified.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges

Chen

Feng

Han

2020

Sensors

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Damage detection of highway bridges is a significant part of structural heath monitoring. Conventional accelerometers or strain gauges utilized for damage detection have many shortcomings, especially their monitoring gauge length being too short, which would result in poor damage detection results. Under this circumstance, long-gauge FBG sensors as a novel optical sensor were developed to measure the macro-strain response of the structure. Based on this sensor, many derived damage detection methods were proposed. These methods exhibit various characteristics and have not been systematically compared. As a result, it is difficult to evaluate the state of the art and also leads to confusion for users to select. Therefore, a strict comparative study on three representative methods using long-gauge FBG was carried out. First, these methods’ theoretical backgrounds and formats were reformulated and unified for better comparison. Then, based on validated vehicle–bridge coupling simulation, these methods’ performances were tested through a series of parametric studies including various damage scenarios, vehicle types, speeds, road roughness and noise levels. The precision and reliability of three methods have been thoroughly studied and compared.

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.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

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Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges

Chen

Feng

Han

2020

Sensors

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“…This method has also been used to identify the settlement and lateral displacement of a statically indeterminate shield tunnel [7]. A theory regarding a long gauge strain influence line coefficient was introduced to detect damage and evaluate the bearing capacity of a bridge [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations

et al. 2018

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Featured Application: The proposed novel approach presented in this paper offers a highly effective and reliable scheme for damage detection and safety assessment of pipeline systems and can be incorporated as a practical and highly reliable structural health monitoring approach for the safe operationty of existing and new pipeline systems.Abstract: Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing.

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“…The availability of long-gauge strain sensors has opened new and interesting possibilities for structural health monitoring. These sensors were proven to be applicable for monitoring at a global structural level through numerous projects [ 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Finite Element Model Updating in Highway Bridge Static Loading Test Using Spatially-Distributed Optical Fiber Sensors

Chen

et al. 2017

Sensors

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A finite model updating method that combines dynamic-static long-gauge strain responses is proposed for highway bridge static loading tests. For this method, the objective function consisting of static long-gauge stains and the first order modal macro-strain parameter (frequency) is established, wherein the local bending stiffness, density and boundary conditions of the structures are selected as the design variables. The relationship between the macro-strain and local element stiffness was studied first. It is revealed that the macro-strain is inversely proportional to the local stiffness covered by the long-gauge strain sensor. This corresponding relation is important for the modification of the local stiffness based on the macro-strain. The local and global parameters can be simultaneously updated. Then, a series of numerical simulation and experiments were conducted to verify the effectiveness of the proposed method. The results show that the static deformation, macro-strain and macro-strain modal can be predicted well by using the proposed updating model.

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Damage identification and bearing capacity evaluation of bridges based on distributed long-gauge strain envelope line under moving vehicle loads

Cited by 30 publications

References 15 publications

Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges

Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges

Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations

Study on Finite Element Model Updating in Highway Bridge Static Loading Test Using Spatially-Distributed Optical Fiber Sensors

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