Corrosion is the biggest enemy of transmission pipelines. According to PHSMA, about 40% of pipeline damage and leakage is the direct result of corrosion, and other types of pipeline damage, such as excavation and natural force damage, are also highly associated with pipeline corrosion and can be easily triggered. Overall, soil corrosivity is the key and influences the degree of corrosion that happens on a pipeline.
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In this section, the objective is to determine the soil corrosivity and assess the corrosion risks of pipelines by developing a continuous soil corrosivity index surface by using ArcMap and ArcGIS Pro in Harris County. Given the fact that soil corrosivity is influenced by a number of different soil properties at different levels, multi-criteria evaluation (MCE) was used as the technique to determine soil corrosivity in Harris County.
Corrosion is the biggest enemy of transmission pipelines. According to PHSMA, about 40% of pipeline damage and leakage is the direct result of corrosion, and other types of pipeline damage, such as excavation and natural force damage, are also highly associated with pipeline corrosion and can be easily triggered. Overall, soil corrosivity is the key and influences the degree of corrosion that happens on a pipeline.
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In this section, the objective is to determine the soil corrosivity and assess the corrosion risks of pipelines by developing a continuous soil corrosivity index surface by using ArcMap and ArcGIS Pro in Harris County. Given the fact that soil corrosivity is influenced by a number of different soil properties at different levels, multi-criteria evaluation (MCE) was used as the technique to determine soil corrosivity in Harris County.
The lack of preparedness of local governments and energy companies led to this serious state-wide power outage and energy crisis. Therefore, to prevent similar series of disasters from happening, it is crucial to ensure and assess the resiliency and integrity of current transmission pipelines in the State of Texas.
In this project, the main objective is to investigate the driving factors of pipeline incidents (failures, accidents) of currently existing pipelines.
The lack of preparedness of local governments and energy companies led to this serious state-wide power outage and energy crisis. Therefore, to prevent similar series of disasters from happening, it is crucial to ensure and assess the resiliency and integrity of current transmission pipelines in the State of Texas.
In this project, the main objective is to investigate the driving factors of pipeline incidents (failures, accidents) of currently existing pipelines.
Result & Discussion
GWR for Human Factors
Exploratory Regression
Four out of the eleven inputted variables were selected from the result of this analysis based on the output report generated by this regression. As mentioned before, the criteria for selecting the most appropriate set of variables is to have the highest adjusted R-squared value and the lowest AICc value. The four variables are:
1. Percent of the Hispanic population
2. Percent of the population living under the poverty line
3. Percent of the male population
4. Total number of household*
*: the input of this variable was normalized by diving 10,000 to have a similar data range with the other four variables.
Generalized Linear Regression
From the GLR results shown below in Map 5.2.1, it can be seen that the standard residuals for most of the incident records in central Harris County were close to what the model estimated. Only several records near Buffalo Bayou and Galveston Bay showed smaller estimated values (negative residuals).
Map 5.2.1: GLR Standardized Residuals
Geographically Weighted Regression
GWR was applied to the pipeline incident density, which is the dependent variable in this study. Below, in Map 5.2.2, the local R-squared results for all of the incident points is shown. A higher R-squared value indicates that the model can fully explain the variation in the dependent variable, which suggests that the model fits the dependent variable well. In contrast, a lower R-squared value means that the model does a poorer job explaining the dependent variable. As shown in the map, the model did fairly a good job explaining local variations for most of the incidents, only incidents located on the southeast part of Downtown Houston show smaller R-squared value.
Map 5.2.2: Local R-squared Results for Pipeline Incident
Other than the R-squared value, coefficient raster layers for each of the four explanatory variables were also generated from the GWR analysis.
Map 5.2.3: GWR results: Percent of the Hispanic population & pipeline incident density
Map 5.2.3 above shows that there is a strong positive relationship between the percent of the Hispanic population with the density of pipeline accidents in the southeast part of Harris County. At the center of the county, a slight negative relationship is also found between them. This result suggests that areas close to Galveston Bay and Buffalo Bayou show a tighter positive relationship between the Hispanic population and pipeline incidents might because the southeast part of Harris County is the major industrial area near Houston, where Hispanic-Americans are likely to work as employees for industrial corporations (US Bureau of Labor Statistics). Currently, Hispanics and Latinos accounted for 16.1% of workers in all kinds of industries.
Map 5.2.4: GWR results: Percent of Male population & pipeline incident density
The results for the male population show a generally positive relationship between the two variables, with the strongest positive relationship located in the southeast part of the county. The only region that shows a negative relationship is an elliptical-shaped area located in the central part of the county. The reason that this pattern is found for the male population percentage is similar to the Hispanic population percentage, males are more likely to look for jobs in heavy industries such as the oil industry and construction industry. Therefore, more males are likely to reside in the southeast areas of the county in order to save time from commuting.
Map 5.2.5: GWR results: Percent of the population living under the poverty line & pipeline incident density
For the percentage of people living under the poverty line, the southeast part of the county once again becomes the place where has the strongest positive relationship. However, to the east of Bear Lake, it is found that this variable is negatively linked to pipeline incidents. Although only 3 to 4 points are located in that area, Chambers County which is located to the east of Harris County has around 20 clustered pipeline incident records located close to the boundary in the city of Mont Belvieu. Therefore, this variation is possibly caused by the clustered points in Chambers County, because the CT where those incidents are located has a relatively low percentage of people living under the poverty line (~6%). In fact, Mont Belvieu is an important petrochemical center in southern Texas where fuels are stored, processed and delivered to the rest of the state. Thus, incidents related to explosive petrochemical products frequently occurred there.
Map 5.2.6: GWR results: Total number of household & pipeline incident density
Generally, the total number of households is positively related to pipeline incidents in southern Harris County. A negative relationship between them is also found around the estuary of the San Jacinto River. Generally, more households in a CT means more excavation activities (house construction or repairment) would happen there. More frequent excavations being proceeded above areas where pipelines are densely built results in a higher probability of damaging the pipeline during excavation processes.