TY - GEN
T1 - Predictive Model Development to Perform Condition Assessment on Pipeline Networks
AU - Rouhanizadeh, Behzad
AU - Kermanshachi, Sharareh
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068746692&partnerID=8YFLogxK
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U2 - 10.1061/9780784482445.004
DO - 10.1061/9780784482445.004
M3 - Conference contribution
AN - SCOPUS:85068746692
T3 - Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 24
EP - 32
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -