Sensitivity of Optical Fiber Sensors to Deflection of Reinforced Concrete Beam

Asriani, Farida; Winasis,; Pamudji, Gandjar

doi:10.1088/1757-899x/982/1/012025

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Selain itu juga telah dilakukan deteksian kerusakan pada beton bertulang menggunakan fiber optik yang tertanam di dalamnya. Penurunan kualitas beton akibat pembebanan bisa dideteksi dari adanya losses pada fiber optik [6], [7], [8].…”

Section: Pendahuluanunclassified

Aplikasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Retakan Beton

Asriani,

Pamudji,

Susilawati

et al. 2023

DINAREK

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Beton menjadi bahan utama dari kebanyakan kontruksi bangunan. Timbulnya sebuah retakan atau kerusakan struktur dari beton tersebut sangat berpengaruh terhadap struktur bangunan secara keseluruhan karena mampu memperpendek umur dari bangunan tersebut. Dari hal tersebut, diperlukannya pengawasan secara rutin terhadap kondisi struktur beton sehingga dapat dilakukan perencanaan pemeliharaan di masa depan. Pada paper ini penulis menerapkan teknologi sistem cerdas terhadap pendeteksian keretakan beton. Penerapan Deep Learning dengan arsitektur Convolutional Neural Networks dengan model MobileNet V1 dan Inception V3 dan ResNet-50 untuk melakukan pengklasifikasian kondisi keretakan dari sebuah masukan gambar visual. Deteksi keretakan beton yang dilakukan dikelompokkan dalam tiga kelas yaitu retak besar, retak kecil dan tidak retak. Dari hasil training dan validasi yang telah dilakukan CNN dengan model mobileNet V1 memberikan hasil akurasi yang terbaik yaitu 0,8924 untuk akurasi pelatihan dan 0,8899 untuk akurasi validasi

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Section: Pendahuluanunclassified

Aplikasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Retakan Beton

Asriani,

Pamudji,

Susilawati

et al. 2023

DINAREK

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“…According to the nature of the signal, FOS technology can be divided into Raman effect-enabled sensing technology, interference effect-enabled sensing technology, Brillouin effect-enabled sensing technology, forward transmission mode coupling-enabled sensing technology, and backward Rayleigh scattering-enabled sensing technology. Distributed intelligent FOS technology is mostly used in the application of OF-based Alarm Systems (AS) outside China [5]. e United States was the first to apply OF technology.…”

Section: Introduction E Development Of Optical Communication and Opticalmentioning

confidence: 99%

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

Gao

Cai

2022

Wireless Communications and Mobile Computing

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To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.

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“…It is widely used to measure physical quantities such as stress and strain in engineering projects such as bridge construction, pipeline leakage and deepwater riser (Zhao et al, 2021). Researchers have proposed various types of optical fiber stress sensors (Asriani et al, 2020;Cai et al, 2020;Guo et al, 2020;Tan et al, 2020;Zheng et al, 2020;Tang et al, 2021;Xiang, 2021), which are based on single-mode multi-mode single-mode tapered fiber Bragg grating sensors. The measurement range of the sensor is 0-960 με; A Fabry Perot based strain sensor (He et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

Qiao

Wang

2022

Front. Energy Res.

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In order to further improve the measurement range and accuracy of optical fiber stress sensor based on the interference between rising vortex beam and plane wave beam, a new stress demodulation model is designed. This model proposes a method to optimize the long-term and short-term memory network (LSTM) model by using sparrow search algorithm (SSA), extract the main characteristics of the influence of various variables on optical fiber stress sensor, and fit the relationship between sensor stress and beam phase difference. This method is an attempt of the deep learning model LSTM in the study of stress mediation model. There are very few related studies, and it is very necessary to fill this gap. In the experiment, the SSA-LSTM neural network is trained by using the data of stress and phase difference measured by the optical fiber stress sensor. The test results show that the mean error of SSA-LSTM neural network is less than that of LSTM neural network, which shows that the combination of SSA-LSTM model and optical fiber stress sensor can make its measurement accuracy higher, The algorithm can more effectively reduce the influence of the surrounding environment and the influence of the light source fluctuation on the measurement range and accuracy of the optical fiber sensor, and has good practical application value. It is proved that the deep learning LSTM neural network has good application value in the light intensity optimization of optical fiber stress sensor.

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Sensitivity of Optical Fiber Sensors to Deflection of Reinforced Concrete Beam

Cited by 3 publications

References 4 publications

Aplikasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Retakan Beton

Aplikasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Retakan Beton

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

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