ANN-Based Fatigue Strength of Concrete under Compression

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.

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ANN-based Fatigue Strength of Concrete Under Compression

10.36227/techrxiv.12672185 ◽

2020 ◽

Author(s):

Abambres M ◽

Lantsoght E

Keyword(s):

Neural Networks ◽

Compressive Strength ◽

Artificial Neural Networks ◽

Fatigue Strength ◽

Relative Error ◽

Maximum Relative Error ◽

Ann Model ◽

Concrete Compressive Strength ◽

Design Code ◽

Number Of Cycles

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.

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ANN-based Fatigue Strength of Concrete Under Compression

10.36227/techrxiv.12672185.v1 ◽

2020 ◽

Author(s):

Abambres M ◽

Lantsoght E

Keyword(s):

Neural Networks ◽

Compressive Strength ◽

Artificial Neural Networks ◽

Fatigue Strength ◽

Relative Error ◽

Maximum Relative Error ◽

Ann Model ◽

Concrete Compressive Strength ◽

Design Code ◽

Number Of Cycles

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.

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Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

Applied Sciences ◽

10.3390/app8081395 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1395 ◽

Cited By ~ 3

Author(s):

Zbigniew Lechowicz ◽

Masaharu Fukue ◽

Simon Rabarijoely ◽

Maria Sulewska

Keyword(s):

Neural Networks ◽

Shear Strength ◽

Artificial Neural Networks ◽

Relative Error ◽

Undrained Shear Strength ◽

Organic Soils ◽

Maximum Relative Error ◽

Empirical Methods ◽

Pressure Reading ◽

Undrained Shear

The undrained shear strength of organic soils can be evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical correlations presented in the literature. However, the empirical methods may sometimes show relatively high values of maximum relative error. Therefore, a method for evaluating the undrained shear strength of organic soils using artificial neural networks based on data obtained from a dilatometer test and organic soil properties is presented in this study. The presented neural network, with an architecture of 5-4-1, predicts the normalized undrained shear strength based on five independent variables: the normalized net value of a corrected first pressure reading (po − uo)/σ′v, the normalized net value of a corrected second pressure reading (p1 − uo)/σ′v, the organic content Iom, the void ratio e, and the stress history indictor (oc or nc). The neural model presented in this study provided a more reliable prediction of the undrained shear strength in comparison to the empirical methods, with a maximum relative error of ±10%.

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An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

10.1101/711309 ◽

2019 ◽

Author(s):

René Janßen ◽

Jakob Zabel ◽

Uwe von Lukas ◽

Matthias Labrenz

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Environmental Monitoring ◽

Microbial Communities ◽

List Type ◽

Neural Net ◽

Next Generation ◽

Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

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Structured Pyramidal Neural Networks

International Journal of Neural Systems ◽

10.1142/s0129065717500216 ◽

2018 ◽

Vol 28 (05) ◽

pp. 1750021 ◽

Cited By ~ 3

Author(s):

Alessandra M. Soares ◽

Bruno J. T. Fernandes ◽

Carmelo J. A. Bastos-Filho

Keyword(s):

Neural Network ◽

Neural Networks ◽

Processing Time ◽

Receptive Fields ◽

Memory Capacity ◽

The Other ◽

Support Vector ◽

Proposed Model ◽

Novel Method ◽

Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

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BP Neural Network Based Animation Production Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.475 ◽

2014 ◽

Vol 539 ◽

pp. 475-478

Author(s):

Ran Tao ◽

Da Chao Yuan ◽

Gang Yi Hu

Keyword(s):

Neural Network ◽

Relative Error ◽

Bp Neural Network ◽

Prediction Method ◽

Coefficient Of Determination ◽

Mean Relative Error ◽

Training Samples ◽

The Mean ◽

Production Prediction

In order to research the basic condition of animation production, this article chooses BP Neural Network to predict the animation production. We select 13 test samples, selected nine of them randomly as training samples, and the remaining four as the test samples. The coefficient of determination is 0.99839 and the mean relative error is 0.186125. The result shows that BP Neural Network is an effective prediction method.

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Quality Prediction of Laser Cladding Based on Evolutionary Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.1012 ◽

2010 ◽

Vol 44-47 ◽

pp. 1012-1017

Author(s):

Zhao Mei Xu ◽

Hai Bing Wu ◽

Zong Hai Hong

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Laser Cladding ◽

Back Propagation ◽

Maximum Relative Error ◽

Technical Parameters ◽

Evolutionary Neural Network ◽

Real Value ◽

Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser cladding forming. The prediction model of surface quality in laser cladding parts, including the width, depth of cladding layer and dilution rate, was proposed based on the improved learned arithmetic. The model combined the global optimization searching performance of the genetic algorithm and localization of the back propagation(BP) neural networks. Five technical parameters were selected to test the reliability of the mode. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2.14% between the predicted content and the real value.

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Handwritten Character and Digit Recognition Using Convolutional Neural Network

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-v4-i3-023 ◽

2021 ◽

pp. 147-155

Author(s):

Priyank Patel ◽

Roshan Shinde ◽

Siddhesh Raut ◽

Sheetal Mahadik

Keyword(s):

Neural Network ◽

Neural Networks ◽

Word Recognition ◽

Convolutional Neural Network ◽

Neural Net ◽

Digit Recognition ◽

Handwritten Character ◽

On Line ◽

Word Division ◽

Precise Content

The necessity for quick and precise content section on little handheld PCs has prompted a resurgence of interest in on-line word recognition utilizing counterfeit neural Networks. Old style strategies are consolidated and improved to give strong recognition of hand-printed English content. The focal idea of a neural net as a character classifier gives a legitimate base to are cognition framework; long-standing issues comparative with preparing, speculation, division, probabilistic formalisms, and so forth, need to settled, notwithstanding, to instigate astounding execution. assortment of developments in a manner to utilize a neural net as a classifier in a very word recognizer are introduced: negative preparing, stroke twisting, adjusting, standardized yield blunder, mistake accentuation, numerous portrayals, quantized loads, and incorporated word division all add to effective and hearty execution.

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Automatic Assessment of Buildings Location Fitness for Solar Panels Installation Using Drones and Neural Network

CivilEng ◽

10.3390/civileng2040056 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1052-1064

Author(s):

Ammar Alzarrad ◽

Chance Emanuels ◽

Mohammad Imtiaz ◽

Haseeb Akbar

Keyword(s):

Neural Network ◽

Neural Networks ◽

Solar Energy ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Solar Panels ◽

Automatic Assessment ◽

Surrounding Environment ◽

Proposed Model ◽

The City

Solar panel location assessment is usually a time-consuming manual process, and many criteria should be taken into consideration before deciding. One of the most significant criteria is the building location and surrounding environment. This research project aims to propose a model to automatically identify potential roof spaces for solar panels using drones and convolutional neural networks (CNN). Convolutional neural networks (CNNs) are used to identify buildings’ roofs from drone imagery. Transfer learning on the CNN is used to classify roofs of buildings into two categories of shaded and unshaded. The CNN is trained and tested on separate imagery databases to improve classification accuracy. Results of the current project demonstrate successful segmentation of buildings and identification of shaded roofs. The model presented in this paper can be used to prioritize the buildings based on the likelihood of getting benefits from switching to solar energy. To illustrate an implementation of the presented model, it has been applied to a selected neighborhood in the city of Hurricane in West Virginia. The research results show that the proposed model can assist investors in the energy and building sectors to make better and more informed decisions.

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