Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Li, Sheng; Zuo, Xiang; Li, Zhengying; Wang, Honghai

doi:10.3390/s20030911

Cited by 29 publications

(14 citation statements)

References 49 publications

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“…The back-propagation algorithm was used for its simplicity and its ability to extract useful information from examples. In fact, its ability to implicitly stores information in the form of connection weights and its applicability to digitally or analog models [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Road infrastructures systems are critical in many regions of Italy, counting thousands of bridges and viaducts that were built over several decades. A monitoring system is therefore necessary to monitor the health of these bridges and to indicate whether they need maintenance. Different parameters affect the health of an infrastructure, but it would be very difficult to install a network of sensors of various kinds on each viaduct. For this purpose, we want to finalize the use of geomatics technologies to monitor infrastructures for early warning issues and introducing automations in the data acquisition and processing phases. This study describes an experimental sensor network system, based on long term monitoring in real-time while an adaptive neuro-fuzzy system is used to predict the deformations of GPS-bridge monitoring points. The proposed system integrates different data (used to describe the various behaviour scenarios on the structural model), and then it reworks them through machine learning techniques, in order to train the network so that, once only the monitored parameters (displacements) have been entered as input data, it can return an alert parameter. So, the purpose is to develop a real-time risk predictive system that can replicate various scenarios and capable to alert, in case of imminent hazards. The experimentation conducted in relation to the possibility of transmitting an alert parameter in real time (transmitted through the help of an experimental control unit) obtained by predicting the behavior of the structure using only displacement data during monitoring is particularly interesting.

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Section: Resultsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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“…Providing a solution to feed this information is of sufficient importance to influence accuracy. For this purpose, Sajedi and Liang [66] developed a grid environment methodology for the real-time damage segmentation in large scale civil infrastructures. They used a fully convolutional encoder-decoder neural network that was trained by cumulative intensity measures as the input and damage states of nodes as output.…”

Section: Vibration-based Shm Through DLmentioning

confidence: 99%

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

411

158

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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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“…For example, Teng et al [70] used the structural MSEn, obtained through finite element simulation, to construct the training data for the proposed convolutional neural network (CNN) model for damage detection of steel frame structures. Khodabandehlou et al [71] proposed a vibration-based structural condition assessment method using acceleration response histories and a two-dimensional DCNN. The proposed method was validated through vibration data which was recorded during the extensive shake-table testing of a highway bridge model at the University of Nevada, Reno.…”

Section: Introductionmentioning

confidence: 99%

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy

Lê

Truong

Dinh-Cong

et al. 2021

Front. Struct. Civ. Eng.

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This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

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Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Cited by 29 publications

References 49 publications

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy

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