Conceptual Cost Modelling for Sustainable Construction Project Planning— A Levenberg–Marquardt Neural Network Approach

2019 ◽  
Vol 13 (2) ◽  
pp. 201-208 ◽  
Author(s):  
P. S. Kumar ◽  
S. Gururaj
Keyword(s):  
Neural Network ◽  
Construction Project ◽  
Project Planning ◽  
Sustainable Construction ◽  
Network Approach ◽  
Neural Network Approach ◽  
Cost Modelling ◽  
Levenberg Marquardt

scholarly journals Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10 ◽  
Author(s):  
Hafiz Waqar Ahmad ◽  
Jeong Ho Hwang ◽  
Kamran Javed ◽  
Umer Masood Chaudry ◽  
Dong Ho Bae
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Dissimilar Material ◽  
Bayesian Regularization ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accelerated Life ◽  
Levenberg Marquardt

Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and artificial neural network approach (ANN). The accelerated life testing approach was evaluated for different distributions. Weibull distribution was the most appropriate distribution that fits the fatigue data very well. Acceleration of fatigue life test data was attained with 95% reliability for Weibull distribution. The probability plot verified that accelerating variables at each level were appropriate. Experimental test data and predicted fatigue life were in good agreement with each other. Two training algorithms, Bayesian regularization (BR) and Levenberg–Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg–Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints.

Performance of the Levenberg–Marquardt neural network approach in nuclear mass prediction

2017 ◽  
Vol 44 (4) ◽  
pp. 045110 ◽  
Author(s):  
Hai Fei Zhang ◽  
Li Hao Wang ◽  
Jing Peng Yin ◽  
Peng Hui Chen ◽  
Hong Fei Zhang
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Nuclear Mass ◽  
Neural Network Approach ◽  
Levenberg Marquardt

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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