GPS, Accelerometer, and Smartphone Fused Smart Sensor for SHM on Real‐Scale Bridges

Guzman-Acevedo, G. Michel; Vázquez-Becerra, G. Esteban; Millán-Almaraz, Jesús Roberto; Rodríguez‐Lozoya, Héctor E.; Reyes‐Salazar, Alfredo; Gaxiola‐Camacho, J. Ramon; Martinez-Felix, Carlos A.

doi:10.1155/2019/6429430

Cited by 36 publications

(26 citation statements)

References 36 publications

(69 reference statements)

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“…Some sensors integrated inside the smartphone may contribute valuable information for SHM [11,30]. Moreover, most modern smartphones' hardware capacity and perfor-mance are sufficient for data analysis, if the corresponding software is adapted [17]. However, most of these sensors are installed or glued at the surface of the structures.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Wang¹,

Chakraborty²,

Niederleithinger³

2021

J Nondestruct Eval

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Reinforced concrete bridges are iconic parts of modern infrastructure. They are designed for a minimum service life of 100 years. However, environmental factors and/or inappropriate use might cause overload and accelerate the deterioration of bridges. In extreme cases, bridges could collapse when necessary maintenance lacks. Thus, the permanent monitoring for structure health assessment has been proposed, which is the aim of structural health monitoring (SHM). Studies in laboratories have shown that ultrasonic (US) coda wave interferometry (CWI) using diffuse waves has high sensitivity and reliability to detect subtle changes in concrete structures. The creation of micro-cracks might be recognized at an early stage. Moreover, large-volume structures can be monitored with a relatively small number of US transducers. However, it is still a challenge to implement the CWI method in real SHM practical applications in an outdoor environment because of the complex external factors, such as various noise sources that interfere with the recorded signals. In this paper, monitoring data from a 36-m long bridge girder in Gliwice, Poland, instrumented with embedded US transducers, thermistors, and vibrating wire strain gauges, is presented. Noise estimation and reduction methods are discussed, and the influence of traffic, as well as temperature variation, are studied. As a result, the relative velocity variation of US waves following the temperature change with a very high precision of $$10^{-4} \%$$ 10 - 4 % is shown, and a good bridge health condition is inferred. The influence of lightweight real traffic is negligible. The study verified the feasibility of the implementation of the CWI method on real bridge structures.

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Section: Introductionmentioning

confidence: 99%

Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Wang¹,

Chakraborty²,

Niederleithinger³

2021

J Nondestruct Eval

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show abstract

“…On the downside, these systems are too costly to construct, the sensors cannot be calibrated periodically, and the layout of monitoring points is limited by local terrain and structure type. e geometric deformation of the bridge structure can only be collected by a few discrete monitoring points, making it difficult to characterize the local or global holographic geometry of bridge safety [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Identification of Behavioral Features of Bridge Structure Based on Static Image Sequences

Deng

Zhou

Chu

et al. 2020

Advances in Civil Engineering

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This paper aims to further enhance the accuracy and efficiency of large bridge structural health monitoring (SHM) through noncontact remote sensing (NRS). For these purposes, the authors put forward an intelligent NRS method that collects the holographic geometric deformation of the test bridge, using the static image sequences. Specifically, a uniaxial automatic cruise acquisition device was designed to collect the dynamic and static images on bridge facade under different damage conditions. Considering the strong spatiotemporal correlations of the sequence data, the relationships between the time history images in six fixed fields of view were identified through deep learning under spatiotemporal sequences. On this basis, the behavioral features of the bridge structure were obtained under vehicle load. Finally, the global holographic deformation of the test bridge and the envelope spectrum of the global holographic deformation were derived from the deformation data. The research results show that the output data of our NRS method were basically consistent with the finite-element prediction (maximum error: 11.11%) and dial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the bridge structure under different damage conditions and can capture the deformation in a continuous and accurate manner. Compared with the limited number of measuring points, holographic deformation data also shows higher sensitivity in damage identification.

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“…On the downside, these systems are too costly to construct, the sensors cannot be calibrated periodically, and the layout of monitoring points is limited by a local terrain and the structure type. The geometric deformation of the bridge structure can only be collected by a few discrete monitoring points, making it difficult to characterize the local or global holographic geometry of bridge safety [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

et al. 2020

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This paper aims to achieve a large bridge structural health monitoring (SHM) efficiently, economically, credibly, and holographically through noncontact remote sensing (NRS). For these purposes, the author proposes a NRS method for collecting the holographic geometric deformation of test bridge, using static image sequences. Specifically, a uniaxial automatic cruise acquisition device was designed to collect static images on bridge elevation under different damage conditions. Considering the strong spatiotemporal correlations of the sequence data, the relationships between six fixed fields of view were identified through the SIFT algorithm. On this basis, the deformation of the bridge structure was obtained by tracking a virtual target using the optical flow algorithm. Finally, the global holographic deformation of the test bridge was derived. The research results show that: The output data of our NRS method are basically consistent with the finite-element prediction (maximum error: 11.11%) and dial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the bridge structure under different damage conditions, and can capture the deformation in a continuous and accurate manner. The research findings lay a solid basis for structure state interpretation and intelligent damage identification.

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GPS, Accelerometer, and Smartphone Fused Smart Sensor for SHM on Real‐Scale Bridges

Cited by 36 publications

References 36 publications

Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Identification of Behavioral Features of Bridge Structure Based on Static Image Sequences

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

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