scholarly journals Application of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweight concrete

2021 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Gokhan Calis ◽  
Sadık Alper Yıldızel ◽  
Ülkü Sultan Keskin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flexural Strength ◽  
Lightweight Concrete ◽  
Basalt Fiber ◽  
Good Alternative ◽  
Superior Performance ◽  
Contributing Factors ◽  
Artificial Neural ◽  
Compressive And Flexural Strengths

Concrete is known as one of the fundamental materials in construction with its high amount of use. Lightweight concrete (LWC) can be a good alternative in reducing the environmental effect of concrete by decreasing the self-weight and dimensions of the structure. In order to reduce self-weight of concrete artificial aggregates, some of which are produced from waste materials, are utilized, and it also contributes to develop a sustainable material Artificial neural networks have been the focus of many scholars for long time with the purpose of analyzing and predicting the lightweight concrete compressive and flexural strengths. The artificial neural network is more powerful method in terms of providing explanation and prediction in engineering studies. It is proved that the error rate of ANN is smaller than regression method. Furthermore, ANN has superior performance over nonlinear regression model. In this paper, an ANN based system is proposed in order to provide a better understanding of basalt fiber reinforced lightweight concrete. In the regression analysis predicted vs. experimental flexural strength, R-sqr is determined to be 86%. The most important strength contributing factors were analyzed within the scope of this study.

An artificial neural network prediction on physical, mechanical, and thermal characteristics of giant reed fiber reinforced polyethylene terephthalate composite

2021 ◽  
pp. 152808372110648
Author(s):  
Arpitha Gulihonenahali Rajkumar ◽  
Mohit Hemath ◽  
Bharath Kurki Nagaraja ◽  
Shivakumar Neerakallu ◽  
Senthil Muthu Kumar Thiagamani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flexural Strength ◽  
Polyethylene Terephthalate ◽  
Flexural Modulus ◽  
Thermal Characteristics ◽  
Giant Reed ◽  
Fiber Reinforced ◽  
Fiber Concentration ◽  
Artificial Neural

Plant fiber reinforced hybrid polymer composites have had broad applications recently because of their lower cost advantages, lower weight, and biodegradable nature. The present work studies the influence of reinforcing giant reed fiber concentration in polyethylene terephthalate (PET) polymer for their physical, mechanical, and thermal characteristics and determines the optimum loading of giant reed fiber using an artificial neural network (ANN) scheme. Giant reed fiber reinforced PET matrix laminates were manufactured from compression molding with different fiber loadings such as 5 wt.%, 10 wt.%, and 20 wt.%. The mechanical characteristics such as tensile and flexural strength and the laminate’s tensile and flexural modulus were appraised and examined. The maximum value of tensile strength, flexural strength, tensile modulus, and flexural modulus were 5.4 MPa, 26 MPa, 8343 MPa, and 6300 MPa, respectively, for PET2 (10 wt.% of giant reed fiber in PET polymer) composite. Fiber pullout, gaps, and fracture behavior were examined from a scanning electron microscope in the microstructural analysis. A machine learning technique has been recommended to combine artificial intelligence while designing giant reed fiber reinforced polymeric laminates. Using the suggested method, an ANN model has been generated to attain the targeted giant reed fiber concentration for PET composite while gratifying the necessary targeted characteristics. The developed method is very effective and decreases the effort and time of material characterization for huge specimens. It will support the researchers in designing their forthcoming test efficiently.

scholarly journals Power quality enhancement using Artificial Neural Network (ANN) based Dynamic Voltage Restorer (DVR)

2021 ◽  
Vol 309 ◽  
pp. 01100
Author(s):  
Chaitanya Kasala ◽  
Vinay Kumar Awaar ◽  
Praveen Jugge
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Power Quality ◽  
Superior Performance ◽  
Dynamic Voltage Restorer ◽  
Voltage Restorer ◽  
Dynamic Voltage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The power quality, which can affect consumers and their utility, is a key concern of modern power system. The sensitive equipment is damaged by voltage harmonics, sag and swell. Therefore, as usage of sensitive equipment has been increasing, power quality is essential for reliable and secure operation of the power system in modern times. The potential distribution flexible AC transmission system (D-FACTS) device, a dynamic voltage restorer (DVR), is widely used to address problems with non-standard voltage in the distribution system. It induces voltages to preserve the voltage profile and ensures continuous load voltage. In this paper, the voltage sag and swell is compensated by DVR with an artificial neural network (ANN) controller. For the generation of reference voltage for voltage source converter (VSC) switching, and for the voltage conversion from rotating vectors to stationary frame, synchronous reference frame (SRF) theory is applied. The DVR Control Strategy and its performance is simulated using MATLAB software. It is also shown a detailed comparison of the ANN controller with the conventional Proportional Integral controller (PI), which showed ANN controller’s superior performance with less Total Harmonic Distortion (THD).

scholarly journals Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

2021 ◽  
Vol 13 (20) ◽  
pp. 4147
Author(s):  
Mohammed M. Alquraish ◽  
Mosaad Khadr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Superior Performance ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Monthly Streamflow Forecasting

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

scholarly journals Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Mirza Naveed Shahzad ◽  
Muhammad Suleman ◽  
Mirza Ashfaq Ahmed ◽  
Amna Riaz ◽  
Khadija Fatima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Healthy Controls ◽  
Validity And Reliability ◽  
Contributing Factors ◽  
Neural Network Approach ◽  
Cross Sectional ◽  
Compulsive Disorder ◽  
Ann Modeling ◽  
Artificial Neural

The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and 400 healthy controls. The respondents were selected through purposive sampling and interviewed by using the Y-BOCS scale with the addition of a factor, worth of an individual in his family. The validity and reliability of data were assessed through Cronbach’s alpha and confirmatory factor analysis. Artificial Neural Network (ANN) modeling was adopted to determine threatening determinants along with their strength to predict OCD in an individual. The results of ANN modeling depicted 98% accurate classification of OCD patients from healthy controls. The most contributing factors in determining the OCD patients according to normalized importance were the contamination and cleaning (100%); symmetric and perfection (72.5%); worth of an individual in the family (71.1%); aggressive, religious, and sexual obsession (50.5%); high-risk assessment (46.0%); and somatic obsessions and checking (24.0%).

scholarly journals Well-placement optimisation using sequential artificial neural networks

2017 ◽  
Vol 36 (3) ◽  
pp. 433-449 ◽  
Author(s):  
Ilsik Jang ◽  
Seeun Oh ◽  
Yumi Kim ◽  
Changhyup Park ◽  
Hyunjeong Kang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Global Solution ◽  
Search Space ◽  
Superior Performance ◽  
Training Dataset ◽  
Well Placement ◽  
Artificial Neural

In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.

scholarly journals ÁRVORE MODELO FRENTE A UMA REDE NEURAL ARTIFICIAL PARA A MODELAGEM CHUVA-VAZÃO

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 527
Author(s):  
Aline Bernarda Debastiani ◽  
Sílvio Luís Rafaeli Neto ◽  
Ricardo Dalagnol da Silva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Tree ◽  
Superior Performance ◽  
Multi Layer Perceptron ◽  
Rainfall Runoff ◽  
Model Tree ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

O objetivo deste estudo é investigar o desempenho da árvore modelo (M5P) e sua sensibilidade à poda e comparação com o desempenho de uma Rede Neural Artificial (RNA) para a simulação da vazão média diária mensal. A motivação para esta análise está na maior simplicidade e velocidade de processamento da M5P comparado às RNAs e a carência de estudos aplicando este método na modelagem hidrológica. O estudo foi desenvolvido na bacia hidrográfica do Alto Canoas, tendo um delineamento experimental composto por um período de treinamento, um de validação cruzada e dois períodos de testes. A RNA utilizada foi a Multi Layer Perceptron (MLP), implementada no software MATLAB, e a M5P (com e sem poda), disponível do software WEKA. O algoritmo M5P se mostrou sensível à poda em somente metade dos tratamentos. A M5P apresentou bom ajuste na modelagem, porém a RNA apresentou desempenho superior em todos os tratamentos.Palavras-chave: rede neural artificial; árvore de regressão; Bacia do Alto Canoas. MODEL TREE IN COMPARISON TO ARTIFICIAL NEURAL NETWORK FOR RAINFALL-RUNOFF MODELING ABSTRACT: The aim of this study is to investigate the performance of the model tree (M5P) and its sensitivity to pruning and comparison to the performance of an Artificial Neural network (ANN) for the simulation of daily average discharge of the month. The motivation for this analysis is on simplicity and speed of processing M5P compared the RNAs. The study was developed in the Alto Canoas watershed, having an experiment consisting of a training period, a cross-validation and two testing periods. The ANN used was the Multi Layer Perceptron (MLP), implemented in MATLAB software, and M5P (with and without pruning), available from the WEKA software. M5P algorithm proved sensitive to pruning in half of the treatments. The M5P showed good fit in the modeling, but the RNA presented superior performance in all treatments.Keywords: artificial neural network; regression tree; Basin Alto Canoas.

scholarly journals Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

2009 ◽  
Vol 15 (2) ◽  
pp. 103-117 ◽  
Author(s):  
S.K. Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

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