Prediction of surface crown pillar stability using artificial neural networks

Tawadrous, A. S.; Katsabanis, P.D.

doi:10.1002/nag.566

Cited by 52 publications

(21 citation statements)

References 8 publications

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“…Additionally, ANNs are characterized by a high fitting performance. The rapid development of computer hardware has increased the processing capabilities, which have led to achievement of ANN models with less computation time [26]. Therefore, these models have been used in a wide range of applications, including classification, regression, and mapping [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O 3 concentrations changes at different O 3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O 3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O 3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO 2 , and O 3 , and meteorological variables (temperature, relative humidity, and wind speed) measured from January 2012 to December 2013. Seven simulations were performed and the achieved models considered wind speed (at 4.9 m·s −1 ), temperature (at 17.5 • C) and NO 2 (at 26.6 µg·m −3 ) as the variables that determine the change of O 3 behavior. All the achieved models presented a similar fitting performance: R 2 = 0.71-0.72, RMSE = 14.5-14.7 µg·m −3 , and the index of agreement of the second order of 0.91. The combined effect of these variables on O 3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O 3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant.

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Section: Introductionmentioning

confidence: 99%

Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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“…It was conducted on the basis of the commonly known coefficient of determination (R 2 ), root-mean-square error (RMSE), and mean absolute error (MAE) [22,24,27,28,[40][41][42][43][44][45][46][47].…”

Section: Performance Evaluationmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

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“…The BP algorithm has a good acceptance by the community because of its robustness and versatility while providing the most efficient learning procedure for MLP networks [15], [16]. In addition, it is a gradient-descent algorithm which adjusts the weights of ANN by using gradients of their error.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Lee¹,

Aziz²,

Jaafar³

2017

ijacsa

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Abstract-Artificial Neural Network (ANN) design has long been a complex problem because its performance depends heavily on the network topology and algorithm to train the set of synaptic weights. Particle Swarm Optimization (PSO) has been the favored optimization algorithm to complement ANN, but a thorough literature study has shown that there are gaps with current approaches which integrate PSO with ANN, including the optimization of network topology and the unreliable weight training process. These gaps have caused inferior effect on critical Artificial Intelligence (AI) applications and systems, particularly when predicting plant machinery and piping failure due to corrosion. The problem of corrosion prediction in the oil and gas domain remains unanswered due to the lack of a flexible prediction method which targets specific damage mechanisms that caused corrosion. This paper proposes a hybrid prediction method known as the Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN), which integrates several layers of PSO to optimize different parameters of the ANN. The multilayered PSO enables the method to optimize the network topology and train the set of synaptic weights at the same time using a hierarchical optimization approach. Through detailed discussion and literature study, the damage mechanism focused in this research is the CO 2 corrosion and the dataset for this research is obtained from the NORSOK empirical model. The proposed AMPSONN method is tested against BP, MPSO and PSOBP methods on an industrial corrosion dataset for different test conditions. The results showed that AMPSONN performs best on all three problems, exhibiting high classification accuracies and time efficiency.

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Prediction of surface crown pillar stability using artificial neural networks

Cited by 52 publications

References 8 publications

Characterization of Surface Ozone Behavior at Different Regimes

Characterization of Surface Ozone Behavior at Different Regimes

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

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