The oil and gas pipelines are significant assets in Iran. However, these assets are subject to degradation from corrosion. Corrosion causes gradual thinning of the pipelines’ wall leading to leaks or bursts. Allowing a corroding pipeline to continue operation may lead to a finite risk of exceeding the limit state of burst. Codes of practice, such as Modified ASME B31G [1] and DNV F101 [3], among others, have developed relationships to determine the bursting pressure of corroded pipelines. The purpose of this paper is to develop, test, and illustrate a simple spreadsheet-based probabilistic procedure that can be used by practicing engineers to determine the Remaining Useful Life (RUL) of a corroding pipeline, following its first inspection. Modified ASME B31G and DNV F101 equations are used to illustrate this method. As new inspection data regarding the extent of corrosion becomes available, the results can be updated and a new probability of failure can be obtained. The calculated probability of failure is then compared with the target values to determine the remaining life. The approach is equally applicable to both onshore and offshore oil and gas pipelines.

Many distributions have been presented with bathtub-shaped failure rates for real-life data. A two-parameter distribution was defined by Chen (2000). This distribution can have a bathtub-shaped or increasing failure rate function. In this paper, we consider two bivariate models based on the proposed distribution by Chen and use the proposed methods of Marshall and Olkin (1967) in the bivariate case and Marshall and Olkin (1997) in the univariate case. In the second case, their method is generalized to the bivariate case and a new bivariate distribution is introduced. These new bivariate distributions have natural interpretations, and they can be applied in fatal shock models or in competing risks models. We call these new distributions as the bivariate Chen (BCH) distribution and bivariate Chen-geometric (BCHG) distribution, respectively. Moreover, the BCH can be obtained as a special case of the BCHG model. Then, the various properties of the new distributions are investigated. The BCHG distribution has five parameters and the maximum likelihood estimators cannot be obtained in a closed form. We suggest using an EM algorithm that is very easy to implement. Also, Monte Carlo simulations are performed to investigate the effectiveness of the proposed algorithm. Finally, we analyze two real data sets for illustrative purposes.

Aims Time prediction of the main failure is of great assistance in managing the risk involved in landslide occurrence. The complexity of subsurface structure, lack of sufficient information about the slip surface, and complexity of seasonal factors make the prediction more difficult. Most of the solutions proposed for modeling the prediction of the main failure are not efficient and are associated with considerable errors due to the oversimplification. It makes the simultaneous incorporation of all effective factors nearly impossible. In this study, a reliable method was proposed for selecting the appropriate time to analyze the landslide movement and providing the speed threshold leading to the main landslide occurrence in a large-scale rockslide in the Anguran Open-Pit Mine. Materials & Methods In this study, the data set of two years movement of a reliable creep type landslide in Anguran Mine (Zanjan, Iran) were implemented to modify the prediction method suggested by the previous study. The method of this study was a careful comparison of accelerator factors and landslide motion. Findings The independence of the movement speed from the effective factors such as precipitation could be a reliable situation that can be used to predict the critical condition of landslide motion toward final and rapid failure. In this rockslide, 1. 5 million m3 block of stone slid into the open pit. Conclusion The employed method presented in this study allows predicting the occurrence of a final rockslide within a reasonable interval of time and preventing the damage occurred through the timely evacuation of workers and equipment.