Detectability of Bridge-Structural Damage Based on Fiber-Optic Sensing through Deep-Convolutional Neural Networks

Li, Sheng; Sun, Lizhi

doi:10.1061/(asce)be.1943-5592.0001531

Cited by 33 publications

(16 citation statements)

References 38 publications

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“…However, this approach can only obtain the general damage states of the building. Khodabandehlou et al [27] apply CNN to determine the damage state of bridges. Their proposed model can accurately predict damage states with different severity.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

A Deep Learning-Based Framework for Damage Detection With Time Series

et al. 2021

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Natural hazards have caused damages to buildings and infrastructures and economic losses in many countries. Immediate emergency response requires accurate damage detection. In recent studies, advances in deep learning approaches have put forward the development of data-driven damage detection. However, these proposed approaches can successfully classify damaged samples into different damage states without quantifying them. Therefore, we propose a deep learning-based framework for damage detection, which comprises signal processing and damage recognition modules. The signal processing module is introduced to denoise the noisy signals collected from sensors and process the denoised signals via fast Fourier transform. The damage recognition module is characterized by a novel network with the architecture of encoder-decoder-encoder. We train the network with only undamaged samples to capture the pattern in the undamaged state. In the testing stage, given an unknown damage state, we feed damaged samples into the trained network to quantify it by outputting the corresponding probability of damage. We conduct experiments to verify the validity and feasibility of the proposed framework with a dataset of a building subjected to excitations. Our approach can map samples from various damage states to a specific probability of damage, which increases as the intensity of excitation increases. INDEX TERMSEncoder-decoder-encoder, Damage detection, Deep learning, Probability of damage, Signal processing.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

A Deep Learning-Based Framework for Damage Detection With Time Series

et al. 2021

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“…They provided a physical interpretation of the trained convolutional layers indicating their ability of noise filtering, eliminating irrelevant information, and preserving the dominant frequency. Recently, Khodabandehlou et al [61] applied 2D CNN for the overall assessment of concrete bridges while using shake-table tests of a one-fourth scale highway bridge. They implemented a 2D CNN for damage classification that was trained using 40 sets of experimental acceleration records and tested on eight new sets.…”

Section: Vibration-based Shm Through DLmentioning

confidence: 99%

“…Along with the advances in computer vision techniques, vision-based SHM is expected to play a pivotal role in the next generation of SHM. In addition, it has been shown that vibration data can be treated in grid-like images to train deep CNN models [61], which expands DL applications to solve real-world problems. In addition, DL models can be used to predict or minimize the response of complex structures without the need for complex finite element models.…”

Section: Applications In Vision-based and Vibration-based Shmmentioning

confidence: 99%

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

Azimi

Eslamlou

Pekcan

2020

Sensors

408

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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“…Machine learning is the ability of a system to learn from experience and to make decisions without being specifically programmed. These algorithms are often used for condition monitoring of mechanical systems to forecast time of failure, which allows scheduled maintenance and prevent unexpected machinery break downs or structural damage [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Identifying Mechanical Vibration Modes of a Cantilever Using Spectrally Multiplexed Bragg Gratings and Machine Learning

Jantzen

Smith

et al. 2020

14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020)

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In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of 1.1 ± 0.1 g. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data.

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Detectability of Bridge-Structural Damage Based on Fiber-Optic Sensing through Deep-Convolutional Neural Networks

Cited by 33 publications

References 38 publications

A Deep Learning-Based Framework for Damage Detection With Time Series

A Deep Learning-Based Framework for Damage Detection With Time Series

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

Identifying Mechanical Vibration Modes of a Cantilever Using Spectrally Multiplexed Bragg Gratings and Machine Learning

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