Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor-Reference-Cited by-同舟云学术

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

Published:2020-08-12 Issue:11 Volume:43 Page:2653-2667
ISSN:8756-758X
Container-title:Fatigue & Fracture of Engineering Materials & Structures
language:en
Short-container-title:Fatigue Fract Eng Mater Struct

Author:

Ben Seghier Mohamed El Amine¹²^ORCID,Carvalho Hermes³^ORCID,Keshtegar Behrooz⁴^ORCID,Correia José A. F. O.⁵^ORCID,Berto Filippo⁶^ORCID

Affiliation:

1. Division of Computational Mathematics and Engineering, Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Vietnam

2. Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

3. Department of Structural Engineering Federal University of Minas Gerais Belo Horizonte Brazil

4. Department of Civil Engineering, Faculty of Engineering University of Zabol Zabol Iran

5. CONSTRUCT, Faculty of Engineering University of Porto Porto Portugal

6. Department of Engineering Design and Materials Norwegian University of Science and Technology Trondheim Norway

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ffe.13325

Reference53 articles.

1. Probabilistic approach evaluates reliability of pipelines with corrosion defects;El Amine Ben Seghier M;OIL GAS J,2017

2. Reliability analysis of low, mid and high-grade strength corroded pipes based on plastic flow theory using adaptive nonlinear conjugate map

3. Inner Flow-Induced Buckling of Fiber-Reinforced Polymeric Catenary Risers

4. Structural reliability of corroded pipeline using the so-called Separable Monte Carlo method

5. Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method

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