Structural Reliability Assessment of Steel Four-Bolt Unstiffened Extended End-Plate Connections Using Monte Carlo Simulation and Artificial Neural Networks

Author(s):  
Hamidreza Abbasianjahromi ◽  
Somayeh Shojaeikhah
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

Author(s):  
Serkan Eti

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.


2013 ◽  
Vol 34 (4) ◽  
pp. 1169-1180 ◽  
Author(s):  
Majid Dehghani ◽  
Bahram Saghafian ◽  
Farzin Nasiri Saleh ◽  
Ashkan Farokhnia ◽  
Roohollah Noori

2014 ◽  
Vol 496-500 ◽  
pp. 2505-2510 ◽  
Author(s):  
Yun Ji ◽  
Xiao Qing Liu ◽  
Tong Chun Li ◽  
Shuo Li

The performance function tends to be implicit or nonlinear in the evaluation of gravity dam reliability, making it difficult to apply some classical methods, such as JC method, Monte-Carlo simulation etc., as they are supposed to be too time-consuming. One possible solution to this problem may be the introduction of artificial neural networks, among which RBF is featured with faster convergence, better precision and can realize global convergence to some extent. In this paper, the application of RBF in gravity dam reliability is investigated, with some examples presented to convince that its reasonable to put it into use.


Author(s):  
Serkan Eti

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.


2016 ◽  
Vol 33 (7) ◽  
pp. 2019-2044 ◽  
Author(s):  
Ertekin Öztekin

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.


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