# Bayesian Survival Analysis Model for Girth Weld Failure Prediction
## Survival Prediction Model  
### Girth-Weld Failure Causes  
Detailed information or dataset can identify more failure's causes than traditional theory analyses of accident's causes. Initial work in this field focused primarily on the \"tree-type\" accident analysis model to analyze accidents, and risk analysis should be based on internal data analysis [21]. Once the failure-factor data are provided, then a risk model is needed to determine the \"occupation rate\" between the number of unknown failure factors and that of possible known failure factors. In other words, in the whole life cycle of a pipeline, when the failure factors of an accident are already known, the higher the ratio, the higher the probability of an accident. The difference from the traditional analysis method is that the new method can be used to determine the probability of a leak by analyzing the possibility of leaks in specific segments of a pipe or groups defects with the same features, while the traditional method evaluates the possibility of certain types of accidents by analogy with other accidents. It is necessary to change the type of analysis of such accidents, find out the factors causing the accidents, and analyze the \"occupation rate\" as a data-mining rule to identify and determine the pipeline status. According to the \"tree-type\" pipeline accident-cause theory and girth-weld failure data, all the elements that may lead to the failure of girth welds are set to the part of the tree as shown in Figure 1.  
The root cause, direct cause, and indirect cause of pipeline girth-weld cracking or leakage can be summarized and analyzed according to the \"tree-type\" accident-cause model. Then, on the basis of the model, it has been found that the key factors leading to the failure of the pipe girth weld include the following:  
Case 1: Girth weld with defects, such as lack of fusion, crack, data from in-line inspection (ILI) or X-ray. Case 2: Physical problem of girth weld, for example, the fracture toughness of the girth weld does not meet the standard, according to data from lab or field test. Case 3: Geometric problem of girth weld, for example, misaligned girth weld, wall-thickness change, presence of a dent, etc., according to data from ILI, construction record, and nondestructive testing (NDT). Case 4: Girth weld with additional external loads, caused, for example, by lifting and ditching during construction, presence of a weld within or near a pipe bend, ground displacement during operation, accidental loads, caused, for example, by earthquakes and other environmental factors, according to data from ILI, monitoring, etc. Case 5: Special weld, such as tie-in weld, repaired weld, according to data from construction record. Case 6: Non-straight girth weld deviating by over 3 degrees, according to data from ILI, NDT, etc. Figure 1: “Tree-type” accident-cause model for girth weld. Case 7: Imprecise management records, such as one 12 m pipe recorded as a combination of three short segments, construction company management lever, construction culture or background, imprecise information of welding quality or failure recorded by the welding team, and similar parameters. Case 7 as a cause of defect is represented at the root of the tree in the above figure; Cases 1- 3, 6, causing defects during construction, are represented in the tree trunk; Case 5 is linked to Case 7, Case 4 depends on monitoring during operation management and corresponds to the leaves of the above tree. The death of a tree or the breakage of a branch indicates failure. These conclusions can provide guidance for identifying high-rupture-risk girth welds. However, due to the limitations of existing defect detection techniques, many indicators are difficult to quantify and cannot be considered in the risk assessment or evaluation of fitness.