A new artificial neural network-based response surface method for structural reliability analysis

Cheng, Jin; Li, Qiusheng; Xiao, Rucheng

doi:10.1016/j.probengmech.2007.10.003

Cited by 154 publications

(79 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cheng et al [45] proposed an application of the uniform design method (UDM) for the selection of the training data sets in the ANN-based FORM technique. UDM improve quality of selected training data sets, providing the samples scattered over the entire design area as uniformly as possible, turning the set highly representative.…”

Section: Ann Training Set Improvement Techniquesmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

View full text Add to dashboard Cite

Section: Ann Training Set Improvement Techniquesmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

View full text Add to dashboard Cite

“…Neurons played an important role that effected on the general characteristics of network and training time (27). The complexity of relationship among parameters determined the number of neuron in the hidden layer (39). The optimum number of neurons in a hidden layer was found by trial and error.…”

Section: Ann Modelingmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

2013

View full text Add to dashboard Cite

Background: Access to safe drinking water is one of the basic human rights and essential for healthy life. Concerns about the effects of copper on human health have led to numerous guidelines and regulations limiting its concentrations in water. Objectives: The major goal of this study is to demonstrate artificial neural network model of the Chahnimeh1 reservoir water quality (Heavy metal concentration) and show the potential of the ANN for producing models capable of efficient forecasting of Cu concentration. Materials and Methods: Water samples were collected from Chahnimeh1 reservoir which was the most important source of drinking water in Sistan-balochistan and analyzed for physical quality parameters such as: EC (electric conductivity), TDS(total dissolved solids), T(temperature), pH and heavy metal (Cu) concentration using standard methods. In this study, a three-layer artificial neural network (ANN) model was investigated to predict the Cu concentration in the water of Chahnimeh1 reservoir. The input variables are electric conductivity, total dissolved solids, temperature and pH, while the Cu concentration in water is the output. We applied The Levenberg-Marquardt (LM) algorithm to train ANN. Results: According to the ANN outputs, hidden layer with 7 neurons had the best performance for predicting Cu concentration. Evaluation indexes including MSE and R in this article were obtained as 0.00008 and 0.9346; 0.00019 and 0.8612; 0.00014 and 0.9372 for training, validation and testing date sets respectively. Conclusions: As we can see the ANN outputs values are very close to actual Cu concentration, so indicating that predicted values are accurate and the network design is proper and the input variables well suitable for the prediction of Cu concentration.

show abstract

“…A number of 27 training data sets is selected inside the interval ½p -i -0:06p -i; p -i þ 0:06p -i], with mean reference value p -i set as a random variable for each mechanical property and defined in Table 1. The UDM values are selected according to the approach proposed by Cheng et al (2008).…”

Section: Ann-based Udm and Ga Developmentsmentioning

confidence: 99%

“…After selecting table U27(27 10 ) of the UDM (Cheng et al 2008), where columns 1, 4, 6, and 9 must be selected according to the respective accessory table for four variables and discrepancy W(n, P) = 0.1189, the resulting integer code format is presented in Table 2. Then, the interval ½p -i -0:06p -i; p -i þ 0:06p -i] is equally discretized with 27 points and, using the integer code format from Table 2, the actual composition for p ¼ ½E1; E2; Y; S] is obtained, as shown in Table 3.…”

Section: Ann-based Udm and Ga Developmentsmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty propagation in inverse reliability-based design of composite structures

Curnis

Hoffbauer

2010

Int J Mech Mater Des

View full text Add to dashboard Cite

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

show abstract

A new artificial neural network-based response surface method for structural reliability analysis

Cited by 154 publications

References 16 publications

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

Uncertainty propagation in inverse reliability-based design of composite structures

Contact Info

Product

Resources

About