Structural Deformation Sensing Based on Distributed Optical Fiber Monitoring Technology and Neural Network

Hou, Gongyu; Li, Zixiang; Wang, Kai-di; Hu, Jinxin

doi:10.1007/s12205-021-1805-z

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…Machine learning could learn signal features and regularity from large amounts of data and process them. In the field of distributed optical fiber sensing, several machine learning methods have been applied to improve the sensing performance in Brillouin optical time domain analysis (BOTDA) [ 16 , 17 , 18 ], Brillouin optical time domain reflectometer (BOTDR) [ 19 , 20 , 21 ], and phase-sensitive optical time domain reflectometer (Φ-OTDR) [ 22 , 23 , 24 , 25 ]. Yang G. et al proposed a convolutional neural network (CNN) model that consists of a one-dimensional denoising convolutional self-encoder and a one-dimensional residual attention network module; this model could extract both temperature and strain in a BOTDA system with better noise immunity and robustness under the conditions of wider temperature and strain ranges [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Yin

Zhu

Liu

et al. 2023

Sensors

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We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system.

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Section: Introductionmentioning

confidence: 99%

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Yin

Zhu

Liu

et al. 2023

Sensors

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“…This affects most of the self-built houses in Peru, making them uninhabitable and vulnerable to structural collapse [4]. In addition, structural deformation must be controlled to ensure the safety of concrete structures [5]. These are designed primarily for strength, using standards such as the American Concrete Institute (ACI) and Technical Standard E.060 (Reinforced Concrete), where the structure can reach a limited state of damage based on the loss of compressive strength capacity of the concrete [6].…”

Section: Introductionmentioning

confidence: 99%

Determination of Steel Area in Reinforced Concrete Beams Using Data Mining Techniques

Palomino Ojeda,

Pérez Herrera,

Quiñones Huatangari

et al. 2023

RIA

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This study aimed to determine the area of reinforcing steel in rectangular reinforced concrete beams, a critical concern given that a significant proportion of residential structures in Peru do not conform to technical design regulations. Data were collected through a structured form encompassing various design variables, yielding a comprehensive data matrix. The methodology adopted involved Knowledge Discovery in Databases (KDD), executed in several stages: (1) selection, where the InfoGainAttributeEval algorithm was utilized to identify variables influencing the reinforcing steel area; (2) preprocessing, during which anomalous and duplicate data were purged using the Python libraries, Pandas and Numpy; (3) reduction, and (4) data mining. For the latter, Decision Stump, Hoeffding Tree, J48, Logistic Model Trees (LMT), and Random Tree classification algorithms were employed, facilitated by Weka 3.9.4. Accuracy rates of these algorithms were found to be 25, 51.70, 77.97, 78.39, and 88.98% respectively. The Random Tree algorithm, in conjunction with the GP_04 model, provided estimations of the steel area in the beams with a reliability exceeding 88%. The application of this model could enable the optimization of beam design, facilitate cost savings on materials, and enhance structural safety. This research thus presents a significant contribution to the field of structural engineering, particularly in regions where compliance with technical design standards is suboptimal.

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“…Researchers have also combined distributed FBG monitoring technology with neural networks to develop tunnel section deformation monitoring techniques [28]. However, the majority of existing methods primarily measure the convergent deformation in tunnels and are seldom applied to accurately measure the vertical displacement of more complex structures.…”

Section: Introductionmentioning

confidence: 99%

Vertical Displacement Measurement of Tunnel Structures Based on Long-Gauge Fiber Bragg Grating Strain Sensing

Zhang,

Li,

Yuan

et al. 2023

Applied Sciences

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Displacement monitoring systems play a crucial role in ensuring the safety of tunnels. Existing sensing technologies and analysis methods may be insufficient for monitoring tunnel displacement, particularly vertical displacement, due to the harshness of long-term monitoring conditions and the intricacies of structural characteristics. A long-gauge fiber Bragg grating (FBG) sensor can be used to obtain macro- and micro-level information and be connected in series for area sensing. In this study, a novel method was developed which utilizes long-gauge strain sensors to monitor the vertical displacement of a tunnel. This method employs a combination of mechanical analysis and monitoring data to accurately estimate the vertical displacement of the structure from the measured coupled strain. Several key aspects of the proposed method for identifying vertical displacement were investigated, including establishing a separation model of coupled strain on the cross-section, deriving the theory for vertical displacement identification, and determining the sensor layout of the tunnel. A series of simulation tests of a tunnel with a three-hole frame structure confirmed the efficiency and robustness of the proposed method, even when subjected to various loading conditions, noise levels, and sensor layouts. The results of this work may provide valuable insights and practical guidance for the effective and continuous displacement measurement of tunnels, ensuring their structural integrity and operational safety.

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Structural Deformation Sensing Based on Distributed Optical Fiber Monitoring Technology and Neural Network

Cited by 9 publications

References 29 publications

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Determination of Steel Area in Reinforced Concrete Beams Using Data Mining Techniques

Vertical Displacement Measurement of Tunnel Structures Based on Long-Gauge Fiber Bragg Grating Strain Sensing

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