Performance comparison between a statistical model, a deterministic model, and an artificial neural network model for predicting damage from pitting corrosion

Journal Article · · Journal of Research of the National Institute of Standards and Technology; (United States)

DOI:https://doi.org/10.6028/jres.099.047· OSTI ID:6661220

Urquidi-Macdonald, M; Macdonald, D D ^[1]

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1. Pennsylvania State Univ., University Park, PA (United States)

Various attempts have been made to develop models for predicting the development of damage in metals and alloys due to pitting corrosion. These models may be divided into two classes: the empirical approach which employs extreme value statistics, and the deterministic approach based on perceived mechanisms for nucleation and growth of damage. More recently, Artificial Neural Networks (ANNs), a nondeterministic type of model, has been developed to describe the progression of damage due to pitting corrosion. The authors compare the three approaches above--statistical, deterministic, and neural networks. Their goal is to illustrate the advantages and disadvantages of each approach, in order that the most reliable methods may be employed in future algorithms for predicting pitting damage functions for engineering structures. To illustrate the difficulty that one faces in predicting cumulative pitting damage, the authors selected a set of data that was collected in the laboratory. They compare and contrast the three approaches by reference to this data set.

• OSTI ID: 6661220
• Journal Information: Journal of Research of the National Institute of Standards and Technology; (United States), Journal Name: Journal of Research of the National Institute of Standards and Technology; (United States) Vol. 99:4; ISSN 1044-677X; ISSN JRITEF
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
360105* -- Metals & Alloys-- Corrosion & Erosion
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ALLOYS
CHEMICAL REACTIONS
COMPARATIVE EVALUATIONS
CORROSION
ELEMENTS
EQUATIONS
EVALUATION
HEAT EXCHANGERS
MATHEMATICAL MODELS
METALS
PERFORMANCE
PITTING CORROSION
PREDICTION EQUATIONS