scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

scholarly journals Diagnosis and Assessment of Pre-Fog in the Mainland Portuguese International Airports: Statistical and Neural Network Models Comparison

2021 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Pedro M. P. Guerreiro ◽  
Gonçalo Cruz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Windows ◽  
Cooling Water ◽  
Onset Time ◽  
Network Models ◽  
Weather Forecast ◽  
Small Scale ◽  
Neural Network Models ◽  
Local Character

The prediction of fog is a challenging task in operational weather forecast. Due to its dependency on small-scale processes, numerical weather models struggle to deal with under scale features, resulting in uncertainties in the fog forecast. Unawareness of the onset time and the duration of fog leads to disproportionate impact on open-air activities, especially in aviation. Nevertheless, in a small sized country such as Portugal mainland, the fog varies greatly. The traffic of the two busiest Portuguese international airports of Porto and Lisbon is affected by the occurrence of fog at different times of the year. The fog occurrence at Porto is a predominant winter phenomenon and a summer one at Lisbon. Observational variables and their trend are local indicators of favouring conditions to the fog’s onset, such as cooling, water vapour saturation and turbulent mixing. A dataset corresponding to 17 years of half-hourly METAR from the airports of Porto and Lisbon is used to diagnose the pre-fog conditioning. Two diagnostic models are proposed to assess pre-fog conditions. The first model is adapted from the statistical method proposed by Menut et al. (2014), which performs a diagnosis from key variables trend, such as temperature, wind speed and relative humidity. Thresholds are defined from the METAR samples in the 6 h period prior to the formation of fog. Due to the local character of fog, the presented thresholds are the most appropriate ones for each airport. The predictability of fog is then assessed using observations. The second approach consists of neural networks such as a fully connected (FC) network and a recurrent neural network (RNN), which are especially well suited for time series. By experimenting with different types of neural networks (NN), we will try to capture the connection between the temporal evolution of measured variables in the dataset and the fog onset. These experiments will include different time windows to measure its influence on prediction performance.

scholarly journals Models for Predicting Development Effort of Small-Scale Visualization Projects

2018 ◽  
Vol 27 (3) ◽  
pp. 413-431
Author(s):  
M.A. Jayaram ◽  
T.M. Kiran Kumar ◽  
H.V. Raghavendra
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Models ◽  
Small Scale ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Neural Network Models ◽  
Grade Point ◽  
Postgraduate Students

Abstract Software project effort estimation is one of the important aspects of software engineering. Researchers in this area are still striving hard to come out with the best predictive model that has befallen as a greatest challenge. In this work, the effort estimation for small-scale visualization projects all rendered on engineering, general science, and other allied areas developed by 60 postgraduate students in a supervised academic setting is modeled by three approaches, namely, linear regression, quadratic regression, and neural network. Seven unique parameters, namely, number of lines of code (LOC), new and change code (N&C), reuse code (R), cumulative grade point average (CGPA), cyclomatic complexity (CC), algorithmic complexity (AC), and function points (FP), which are considered to be influential in software development effort, are elicited along with actual effort. The three models are compared with respect to their prediction accuracy via the magnitude of error relative to the estimate (MER) for each project and also its mean MER (MMER) in all the projects in both the verification and validation phases. Evaluations of the models have shown MMER of 0.002, 0.006, and 0.009 during verification and 0.006, 0.002, and 0.002 during validation for the multiple linear regression, nonlinear regression, and neural network models, respectively. Thus, the marginal differences in the error estimates have indicated that the three models can be alternatively used for effort computation specific to visualization projects. Results have also suggested that parameters such as LOC, N&C, R, CC, and AC have a direct influence on effort prediction, whereas CGPA has an inverse relationship. FP seems to be neutral as far as visualization projects are concerned.

scholarly journals Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

2020 ◽  
Vol 69 (7-8) ◽  
pp. 355-364
Author(s):  
Souad Belmadani ◽  
Mabrouk Hamadache ◽  
Cherif Si-Moussa ◽  
Maamar Laidi ◽  
Salah Hanini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Kinematic Viscosity ◽  
Binary Systems ◽  
Correlation Coefficients ◽  
Network Models ◽  
Neural Network Models ◽  
Experimental Database ◽  
Artificial Neural

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results.

scholarly journals An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

2019 ◽  
Vol 38 (2) ◽  
pp. 270-281 ◽  
Author(s):  
Mohammad Ghaderi ◽  
Hossein Javadikia ◽  
Leila Naderloo ◽  
Mostafa Mostafaei ◽  
Hekmat Rabbani
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Correlation Coefficients ◽  
Network Models ◽  
Noise Pollution ◽  
Neural Network Models ◽  
Diesel Fuels ◽  
Artificial Neural Network Models ◽  
Mean Square Errors

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in MF285 Tractor was studied in the second and third gears from two positions: driver and bystander, at 1000 and 1600 r/min, and running on 10 different fuel levels. For data analysis, the ANFIS network, neural network, and response surface methodology were applied. Comparing the means of noise pollution at different levels demonstrated that the B25E6D69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 r/min. Although the noise pollution emitted in the third gear was a little more than that emitted in the second gear. All the resultant models, laid by response surface methodology, neural network, and ANFIS had excellent results. Considering the statistical criteria, the best models with high correlation coefficients and low mean square errors were ANFIS, response surface methodology, and artificial neural network models, respectively.

scholarly journals Generic and Specific Recurrent Neural Network Models: Applications for Large and Small Scale Biopharmaceutical Upstream Processes

2021 ◽  
pp. e00640
Author(s):  
Jens Smiatek ◽  
Christoph Clemens ◽  
Liliana Montano Herrera ◽  
Sabine Arnold ◽  
Bettina Knapp ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Small Scale ◽  
Neural Network Models

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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