Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks

2007 ◽  
Vol 22 (3) ◽  
pp. 1202-1209 ◽  
Author(s):  
Armando M. Leite da Silva ◽  
Leonidas Chaves de Resende ◽  
Luiz AntÔnio da Fonseca Manso ◽  
Vladimiro Miranda
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Reliability Assessment ◽  
Artificial Neural ◽  
Composite Reliability

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation

2013 ◽  
Vol 34 (4) ◽  
pp. 1169-1180 ◽  
Author(s):  
Majid Dehghani ◽  
Bahram Saghafian ◽  
Farzin Nasiri Saleh ◽  
Ashkan Farokhnia ◽  
Roohollah Noori
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Uncertainty Analysis ◽  
Artificial Neural

The Use of Quantitative Methods in Investment Decisions

2021 ◽  
pp. 1-20
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

ANN based investigations of reliabilities of the models for concrete under triaxial compression

2016 ◽  
Vol 33 (7) ◽  
pp. 2019-2044 ◽  
Author(s):  
Ertekin Öztekin
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Limit States ◽  
Content Type ◽  
Artificial Neural

Purpose A lot of triaxial compressive models for different concrete types and different concrete strength classes were proposed to be used in structural analyses. The existence of so many models creates conflicts and confusions during the selection of the models. In this study, reliability analyses were carried out to prevent such conflicts and confusions and to determine the most reliable model for normal- and high-strength concrete (NSC and HSC) under combined triaxial compressions. The paper aims to discuss these issues. Design/methodology/approach An analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. After verifying the validity of the model by making comparisons with the models in the literature, reliabilities of all models were investigated. The Monte Carlo simulation method was used in the reliability studies. Artificial experimental data required for the Monte Carlo simulation method were generated by using artificial neural networks. Findings The validity of the proposed model was verified. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Finally, the reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Research limitations/implications Concrete compressive strength and lateral compression were taken as variables in the model. Practical implications The reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Originality/value A new analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Artificial experimental data were obtained by using artificial neural networks. Four different artificial neural networks were developed to generate artificial experimental data. They can also be used in the estimations of the strength of NSC and HSC under different triaxial loadings.

Neural network based stochastic design charts for settlement prediction

2005 ◽  
Vol 42 (1) ◽  
pp. 110-120 ◽  
Author(s):  
M A Shahin ◽  
M B Jaksa ◽  
H R Maier
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Shallow Foundations ◽  
Granular Soils ◽  
Traditional Methods ◽  
Settlement Prediction ◽  
Design Charts ◽  
Artificial Neural ◽  
Level Of Risk

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

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