Using copula method for pipe data analysis

• Copula modeling is introduced to water pipe line data.
• Copula dependency method Spearman’s rho provides a better measure of correlation for pipe variables.
• Copula method gives a better prediction for pipe leakage based on climate factor, temperature of water.
• Vine copula method is used for simulation modeling of multivariate pipe data.

Aging water mains systems are becoming a growing concern for maintenance. The structural deterioration of water mains is affected by different factors such as pipe age, pipe material, soil condition, pipe size, climate condition among others. Since pipes are underground and obtaining data for pipes are difficult and expensive to obtain, various statistical modeling methods have been used to analyze the factors contributing to the pipe condition deterioration and predict the failure of pipes. This paper applies copula method for pipe data analysis. Copula is an emerging method of modeling that has been widely used in financial sectors. There has been recent use in hydrology and bridge management sectors but the method has not been applied to other civil engineering disciplines. Copula method is very useful where marginals belong to different families of distributions. This paper uses copula modeling to determine dependency between several variables of pipe condition and compare how it may be a better choice for determining correlation dependency for data which are non normal and skewed. Copula modeling is used in this paper to predict pipe leakage due to climate condition, in which water temperature is used as a factor. Vine copula is used to develop multi variable simulation models for pipe data. Copula method is very useful and provides better results where marginals belong to different families of distributions, which is observed in the data set used for pipe data analysis.