Predicting capacity model and seismic fragility estimation for RC bridge based on artificial neural network

Structures ◽  
2020 ◽  
Vol 27 ◽  
pp. 1930-1939
Author(s):  
Caigui Huang ◽  
Surong Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Fragility ◽  
Capacity Model ◽  
Artificial Neural

Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network

2020 ◽  
Author(s):  
Shulong Zhang ◽  
Wenxing Zhou ◽  
Shenwei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internal Pressure ◽  
Axial Compression ◽  
Full Scale ◽  
Assessment Model ◽  
Ground Movement ◽  
Ann Model ◽  
Capacity Model ◽  
Artificial Neural

Abstract In-service pipelines are often subjected to longitudinal forces and bending moments resulting from, for example, ground movement or formation of free spans in addition to internal pressures. In practice, there are some site-specific cases where corrosion anomalies interact with the external loads. A refined assessment model is required to understand the load carrying capacity of pipe. In this study, a burst capacity model for corroded pipelines under combined internal pressure and axial compression is developed based on extensive parametric three-dimensional (3D) elasto-plastic finite element analyses (FEA) and artificial neural network (ANN) technique. The parametric FEA employs the ultimate tensile strength (UTS)-based burst criterion and idealizes corrosion defects as semi-ellipsoidal shaped flaws. The FEA model is validated by full-scale burst tests of pipe specimens containing semi-ellipsoidal shaped flaws reported in the literature. Extensive parametric FEA are carried out to evaluate the burst capacity of corroded pipelines under combined internal pressure and axial compression by varying the pipe geometric and material properties, defect depth, length and width, and magnitude of axial compressive stress. Based on the FEA results, an ANN model is developed utilizing the open-source platform PYTHON to predict the burst capacity of corroded pipelines under combined internal pressure and axial compression. The well-trained ANN model is further validated by full-scale burst tests of corroded pipelines under combined internal pressure and axial compression carried out by Det Norske Veritas (DNV).

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural
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