Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC

Kına, Ceren; Türk, Kâzım; Atalay, Esma; Donmez, Izzeddin; Tanyıldızı, Harun

doi:10.1007/s00521-021-05836-8

Cited by 48 publications

(10 citation statements)

References 68 publications

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“…In order to obtain relatively complete information of this type of measured object, it is necessary to use a distributed modulation optical fiber sensing system [14]. The so-called distributed modulation refers to that the external signal field (the measured field) modulates the light waves in the optical fiber in a certain spatial distribution, forms a modulated signal spectrum band in a certain measurement domain, and detects (demodulates) the modulated signal spectrum band The size and spatial distribution of the external signal field can be measured [15].…”

Section: Basic Composition Of Fiber Grating Sensor System and Sensor ...mentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent electrical equipment fiber Bragg grating temperature measurement system

Peng

Liu

Yang

2021

EURASIP J. Adv. Signal Process.

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Fiber Bragg gratings are widely used in electrical equipment monitoring, structural health monitoring, power system fault monitoring and other actual detection occasions due to the unique advantages of anti-electromagnetic interference, low fusion loss, high sensitivity, small size, and easy distributed measurement. Fiber grating has been widely used in actual detection occasions such as electrical equipment monitoring, structural health monitoring, and power system fault monitoring. The temperature measurement of the motor based on fiber grating sensing technology not only has strong anti-electromagnetic interference ability, but also can realize multi-point distributed temperature measurement. The research of fiber grating temperature measurement system for intelligent electrical equipment is of great significance. This article mainly studies the optical fiber grating temperature measurement system of intelligent electrical equipment. This paper designs the temperature measurement system architecture, including data acquisition layer, data monitoring layer and remote monitoring layer. Complete the system design from two aspects: hardware design and software design. In the hardware part, the functions of the temperature sensor module transmission system, signal demodulation system, PLC and host computer are analyzed. Use FTM3501 fiber temperature converter is used to realize the automatic electrical fiber grating. Experimental results show that the thermometer has alarm and display analysis functions. The measurement uncertainty is analyzed, and the uncertainty of the calibration result of the fiber Bragg grating temperature sensor is 0.0725 °C; the fault judgment time of the system in this paper does not exceed 4.73 s, while the fault judgment time of the control group is more than 4.73 s. Compared with the control group, it has a faster fault accuracy speed. The system has high measurement accuracy and good stability, can be applied to actual temperature measurement systems, and has certain practical value.

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Section: Basic Composition Of Fiber Grating Sensor System and Sensor ...mentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent electrical equipment fiber Bragg grating temperature measurement system

Peng

Liu

Yang

2021

EURASIP J. Adv. Signal Process.

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“…Complementarily, the Pearson correlation coefficient (R) was calculated for each neural network and it was found that the results presented are better than those predicted in the literature [2][3][4][5][6][7][8], and [22][23][24][25][26][27][28][29][30][31][32][33][34]. The analysis of Fig.…”

Section: Resultsmentioning

confidence: 95%

“…Since the information on the training error does not provide information on which neural model provides the best generalization, the complexity problem was first solved by dividing the available data into 3 sets, the so-called training set and 2 other sets used for testing and validation to solve the neural model complexity problem [26][27][28][29].…”

Section: Neural Network Architecturementioning

confidence: 99%

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Cardoso

Felice

2021

Int. J. Adv. Intell. Informatics

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The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

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“…The performances of the deep LSTM for the training and testing sets were shown in Figure 9. Kina et al 70 devised ELM and deep LSTM models to predict the fresh properties of hybrid fiber‐reinforced SCC. They said that the ELM model showed better prediction performance than the deep learning model.…”

Section: Prediction Resultsmentioning

confidence: 99%

Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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Deep auto‐encoders and long short‐term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k‐nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self‐compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty‐eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto‐encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber‐reinforced SCC was superior to the other prediction models.

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Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC

Cited by 48 publications

References 68 publications

RETRACTED ARTICLE: Intelligent electrical equipment fiber Bragg grating temperature measurement system

RETRACTED ARTICLE: Intelligent electrical equipment fiber Bragg grating temperature measurement system

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

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