Vol. 10, No. 3, pp. 325-350 (2014)
ULTIMATE CAPACITY ASSESSMENT OF WEB PLATE BEAMS WITH PITTING CORROSION SUBJECTED TO PATCH LOADING BY ARTIFICIAL NEURAL NETWORKS
Yasser Sharifi* and Sajjad Tohidi
Department of Civil Engineering
Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
*(Corresponding author: E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. or This email address is being protected from spambots. You need JavaScript enabled to view it. or)
Received: 17 May 2013; Revised: 19 October 2013; Accepted: 22 October 2013
DOI:10.18057/IJASC.2014.10.3.5
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ABSTRACT
Corrosion is an unavoidable phenomenon in ship hull structures and thickness loss of the structural members due to corrosion is a great concern when the integrity of hull structures is considered. It is well known that pitting corrosion occurring on coated hold frames will surely result in a significant degradation of the ultimate strength of these members. Extensive study on the effect of pitting corrosion on structural strength under a wide variety of loading conditions is necessary to assess the relationship between pitting corrosion intensity and residual strength precisely. The aim of the present study is to investigate the ultimate strength characteristics of steel beams with pit and uniform corrosions wastage. Then pitted member will predict with a member that its thickness decreases uniformly in terms of ultimate strength. A series of ABAQUS nonlinear elastic-plastic analyses by Finite Element Method (FEM) has been carried out on I-shape section steel models, varying the degree of pit corrosion intensity. Load-carrying capacity of deteriorated steel beam models with different pit corrosion under patch loading has been estimated using Artificial Neural Network (ANN) method using FE results. The ultimate strength reduction factor due to web pitting corrosion of steel beams is empirically derived by ANNs of the computed results as a function of DOP. Hence, the results of this study can be used for better prediction of the failure of deteriorated steel beams by practice engineers.
KEYWORDS
Pitting corrosion, steel structures, nonlinear FE analyses, patch loading, artificial neural networks
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