Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of establishing corrosion rate criterion as a function of pipe burial depth, soil types, and properties for the prediction of deterioration of metallic pipe conveying fluid. The proposed model includes a fuzzy model and the artificial neural network (ANN) to determine soil corrosivity potential (CoP) based on soil properties. The combination contains the data of linguistic variables characterising various soil properties, and learning capability of the system that constructs relationships among those soil properties and CoP. Subsequently, the artificial neuro-fuzzy inference system (ANFIS) maps each element of its input membership function to an output membership function between 0 and 1 to determine the deterioration rate (CoP) of metallic fluid-conveying-pipe. Field data from buried fluid pipes were examined to illustrate the application of the proposed model. The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of buried fluid pipes more than any other considered factor. http://dx.doi.org/10.4314/njt.v36i1.10
Influence of soil properties and burial depth on Persian oak (Quercus brantii Lindl.) establishment in different microhabitats resulting from traditional forest practices
