Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predictive Model Development to Perform Condition Assessment on Pipeline Networks
Sewer networks’ performance includes several uncertainties, which increases the corresponding risks of the system. To improve performance of sewer pipeline networks, a useful method is to inspect the operation of the pipes regularly. Since random inspection of pipes is greatly expensive, the pipeline network decision-makers tend to have a predictive model to anticipate the condition of these systems. In this research, a model was developed to predict the performance of pipes, using multiple regression method. This model utilizes historical data as input including the failures, and hydraulic data. The results of this model revealed that the predictive model was more sensitive to the age of the pipeline system as well as the type of pipes. This model assists engineers and decision-makers to identify the pipe segments which are more inclined to failure and helps them to develop a comprehensive risk management system for sewer pipeline systems.
Predictive Model Development to Perform Condition Assessment on Pipeline Networks
Sewer networks’ performance includes several uncertainties, which increases the corresponding risks of the system. To improve performance of sewer pipeline networks, a useful method is to inspect the operation of the pipes regularly. Since random inspection of pipes is greatly expensive, the pipeline network decision-makers tend to have a predictive model to anticipate the condition of these systems. In this research, a model was developed to predict the performance of pipes, using multiple regression method. This model utilizes historical data as input including the failures, and hydraulic data. The results of this model revealed that the predictive model was more sensitive to the age of the pipeline system as well as the type of pipes. This model assists engineers and decision-makers to identify the pipe segments which are more inclined to failure and helps them to develop a comprehensive risk management system for sewer pipeline systems.
Predictive Model Development to Perform Condition Assessment on Pipeline Networks
Rouhanizadeh, Behzad (Autor:in) / Kermanshachi, Sharareh (Autor:in)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
13.06.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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