• Title, Summary, Keyword: Artificial Neural Networks

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Prediction of Consolidation Settlements at Vertical Drain Using Modular Artificial Neural Networks (모듈형 인공신경망을 이용한 연직배수공법에서의 압밀침하량 예측)

  • 민덕기;황광모;전형원
    • Journal of the Korean Geotechnical Society
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    • v.16 no.2
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    • pp.71-77
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    • 2000
  • In this paper, consolidation settlements with time at vertical drain sites were predicted by artificial neural networks. Laboratory test results and field measurements of two vertical drain sites were used for training and testing neural networks. Predicted consolidation settlements by trained artificial neural networks were compared with measured settlements by field instrumentation. To improve the prediction accuracy, modular artificial neural networks were studied. From the results of applying artificial neural networks to the same situation, it was shown that modular artificial neural network model was more accurate for the prediction of the consolidation settlements than the general model.

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A NEW ALGORITHM OF EVOLVING ARTIFICIAL NEURAL NETWORKS VIA GENE EXPRESSION PROGRAMMING

  • Li, Kangshun;Li, Yuanxiang;Mo, Haifang;Chen, Zhangxin
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.9 no.2
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    • pp.83-89
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    • 2005
  • In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

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Squint Free Phased Array Antenna System using Artificial Neural Networks

  • Kim, Young-Ki;Jeon, Do-Hong;Thursby, Michael
    • The Journal of Korean Association of Computer Education
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    • v.6 no.3
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    • pp.47-56
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    • 2003
  • We describe a new method for removing non-linear phased array antenna aberration called "squint" problem. To develop a compensation scheme. theoretical antenna and artificial neural networks were used. The purpose of using the artificial neural networks is to develop an antenna system model that represents the steering function of an actual array. The artificial neural networks are also used to implement an inverse model which when concatenated with the antenna or antenna model will correct the "squint" problem. Combining the actual steering function and the inverse model contained in the artificial neural network, alters the steering command to the antenna so that the antenna will point to the desired position instead of squinting. The use of an artificial neural network provides a method of producing a non-linear system that can correct antenna performance. This paper demonstrates the feasibility of generating an inverse steering algorithm with artificial neural networks.

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The Use of Artificial Neural Networks in the Monitoring of Spot Weld Quality (인공신경회로망을 이용한 저항 점용접의 품질감시)

  • 임태균;조형석;장희석
    • Journal of Welding and Joining
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    • v.11 no.2
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    • pp.27-41
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    • 1993
  • The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

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A Prediction of the Plane Failure Stability Using Artificial Neural Networks (인공신경망을 이용한 평면파괴 안정성 예측)

  • Kim, Bang-Sik;Lee, Sung-Gi;Seo, Jae-Young;Kim, Kwang-Myung
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.513-520
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    • 2002
  • The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

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A Study on Neural Networks Forecast Model of Deep Excavation Wall Movements (인공신경망 기법을 활용한 굴착공사 흙막이 변위량 예측에 관한 연구)

  • Shin, Han-Woo;Kim, Gwang-Hee;Kim, Young-Seok
    • Journal of the Korea Institute of Building Construction
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    • v.7 no.3
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    • pp.131-137
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    • 2007
  • To predict deep excavation wall movements is important in the urban areas considering the cost and the safety in construction. Failing to estimate deep excavation wall movements in advance causes too many problems in the projects. The purpose of this study is to propose the forecast model of deep excavation wall movements using artificial neural networks. The data of the Deep Excavation Wall Movements which were done form Long research is used of Artificial neural networks training and apply the real construction work measured data to the Artificial neural networks model. Applying the artificial neural networks to forecast the deep excavation wall movements can significantly contribute to identifying and preventing the accident in the overall construction work.

Automatic Composition Using Training Capability of Artificial Neural Networks and Chord Progression (인공신경망의 학습기능과 화성진행을 이용한 자동작곡)

  • Oh, Jin-Woo;Song, Jung-Hyun;Kim, Kyung-Hwan;Jung, Sung Hoon
    • Journal of Korea Multimedia Society
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    • v.18 no.11
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    • pp.1358-1366
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    • 2015
  • This paper proposes an automatic composition method using the training capability of artificial neural networks and chord progression rules that are widely used by human composers. After training a given song, the new melody is generated by the trained artificial neural networks through applying a different initial melody to the neural networks. The generated melody should be modified to fit the rhythm and chord progression rules for generating natural melody. In order to achieve this object, we devised a post-processing method such as chord candidate generation, chord progression, and melody correction. From some tests we could find that the melody after the post-processing was very improved from the melody generated by artificial neural networks. This enables our composition system to generate a melody which is similar to those generated by human composers.

The use of neural networks for the prediction of swell pressure

  • Erzin, Yusuf
    • Geomechanics and Engineering
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    • v.1 no.1
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    • pp.75-84
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    • 2009
  • Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The prediction of swell pressures from easily determined soil properties, namely, initial dry density, initial water content, and plasticity index, have been investigated by using artificial neural networks. The results of the constant volume swell tests in oedometers, performed on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties, were trained in an ANNs program and the results were compared with the experimental values. It is observed that the experimental results coincided with ANNs results.

Long-term quality control of self-compacting semi-lightweight concrete using short-term compressive strength and combinatorial artificial neural networks

  • Mazloom, Moosa;Tajar, Saeed Farahani;Mahboubi, Farzan
    • Computers and Concrete
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    • v.25 no.5
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    • pp.401-409
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    • 2020
  • Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.

Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks

  • Asteris, Panagiotis G.;Armaghani, Danial J.;Hatzigeorgiou, George D.;Karayannis, Chris G.;Pilakoutas, Kypros
    • Computers and Concrete
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    • v.24 no.5
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    • pp.469-488
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    • 2019
  • In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.