Artificial neural network method for flawed pipe failure evaluation: Probabilistic models

International Journal of Pressure Vessels and Piping

Volumn 68, Issue 2, 1996, Pages 203-207

  Han, Yu Lin ^a   Dai, Shu Ho ^a  

^a NANJING TECH UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BENDING (DEFORMATION); DEFECTS; FAILURE ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY;

FAILURE BENDING MOMENT; FLAWED PIPE;

PIPE;

FAILURE; NEURAL NETWORKS; PIPES;

EID: 0029675743     PISSN: 03080161     EISSN: None     Source Type: Journal    
DOI: 10.1016/0308-0161(94)00056-5     Document Type: Article

References (6)

1. Han, Y.-L., Shen, S.-M. & Dai, S.-H., Artificial neural network technology as a method to evaluate the failure bending moment of pipe with cicumferential crack. Int. J. Pres. Ves. & Piping, 65 (1996).
2. Zhang, L., Models and Applications of Artificial Neural Networks, Fu Dan University Press, P.R. China, 1993.
3. Pao, Y.-H., Adaptive Pattern Recognition and Neural Networks, Addison-Wesley Publishing Co., 1989.
4. Cichocki, A. & Unbehauen, R., Neural Networks for Optimization and Signal Processing, John Wiley & Sons Ltd., 1993.
5. Herter, K.-H., Julisch, P., Stoppler, W. & Sturm, D., Behavior of Pipes Under Internal Pressure and External Bending Moment - Comparison Between Experiment and Calculation, in Fracture Mechanics Verification by Large-Scale Testing, EGF/ESIS8 (Edited by K. Kussmaul), Mechanical Engineering Publication, London, 1991, 223-241.
6. Dai, S.-H. & Wang, M.-O., Reliability analysis in engineering applications, Van Nostrand Reinhold, 1992, 208-219.