Statistical modelling of corrosion growth in marine environment

• Main Author: Md. Noor, Norhazilan
• Format: Monograph
• Language: English
• Published: Faculty of Civil Engineering 2009
• Subjects: TH Building construction
• Online Access: http://eprints.utm.my/id/eprint/9781/1/78188_Norhazilan_Md_Noor_FKA_TT_2009.pdf; http://eprints.utm.my/id/eprint/9781/

Statistical and probabilistic methods are now recognized as a proper method to address the degree of randomness and complexity of the corrosion process. Nevertheless, the inclusion of this approach within corrosion model development is still rarely practiced in the structure assessment. This has led to the tendency by engineers and inspection personnel to use much simpler approaches in the assessment of corrosion progress. For example, the use of the linear model to predict the future growth of corrosion defects is widely practised despite its questionable accuracy. This work develops several corrosionrelated models based on actual metal loss data with objectives to improve the data interpretation as well as prediction of future defect growth. Although this work deals specifically with data from oil pipelines and vessel’s ballast tanks, the models has been designed to be generic, with no restriction on the types of structure or inspection tool. The procedure consists of three stages: data sampling, data analysis and probabilistic-based prediction. A statistical approach has been applied to model the corrosion parameters as a probability distribution. The issues raised by the presence of negative growth rate and unknown corrosion initiation time have been addressed by the development of new correction methods and a new data sampling technique. The research also demonstrates how the simple linear model can be modified to account for errors arising from the randomness of corrosion growth data and the variation in measured growth for severe defects. A proposed development of the linear-based model has been extensively used in the simulation programme. New data sampling techniques, data correction approaches, and alternative linear models have been developed to improve the assessment work on corrosion data. To conclude, this research was able to demonstrate how inspection data can be more fully utilised to optimise the application of information of corrosion progress to structural analysis.