TY - GEN
T1 - Implementation of Machine Learning Techniques for Prediction of the Corrosion Depth for Water Pipelines
AU - Kim, Taehyeon
AU - Kim, Kibum
AU - Kim, Jinwon
AU - Kim, Jinkeun
AU - Koo, Jayong
N1 - Publisher Copyright:
© ASCE.
PY - 2023
Y1 - 2023
N2 - Water pipelines are crucial components of water supply systems that safely supply water produced by water purification plants to consumers. Metal pipelines deteriorate over time owing to various physical, environmental, and operational factors; in particular, corrosion occurs inside and outside the pipelines due to the characteristics of the pipeline material. In this study, models were developed using machine-learning algorithms to predict internal and external corrosion depth. The hyperparameters of each model were determined through Bayesian optimization, and model training, validation, and prediction were performed. The proposed machine-learning techniques for predicting the corrosion depth of water pipelines can overcome current limitations, such as the prediction of deterioration and residual life of water pipelines and the selection of the diagnostic points of the pipelines. These models may be increasingly valuable with changes in the technological paradigm of diagnostic methods.
AB - Water pipelines are crucial components of water supply systems that safely supply water produced by water purification plants to consumers. Metal pipelines deteriorate over time owing to various physical, environmental, and operational factors; in particular, corrosion occurs inside and outside the pipelines due to the characteristics of the pipeline material. In this study, models were developed using machine-learning algorithms to predict internal and external corrosion depth. The hyperparameters of each model were determined through Bayesian optimization, and model training, validation, and prediction were performed. The proposed machine-learning techniques for predicting the corrosion depth of water pipelines can overcome current limitations, such as the prediction of deterioration and residual life of water pipelines and the selection of the diagnostic points of the pipelines. These models may be increasingly valuable with changes in the technological paradigm of diagnostic methods.
UR - http://www.scopus.com/inward/record.url?scp=85169480401&partnerID=8YFLogxK
U2 - 10.1061/9780784485033.016
DO - 10.1061/9780784485033.016
M3 - Conference contribution
AN - SCOPUS:85169480401
T3 - Pipelines 2023: Condition Assessment, Utility Engineering, Surveying, and Multidiscipline - Proceedings of Sessions of the Pipelines 2023 Conference
SP - 139
EP - 148
BT - Pipelines 2023
A2 - Ellenberger, Christine S.
A2 - Shirk, Jonathan D.
PB - American Society of Civil Engineers (ASCE)
T2 - Pipelines 2023 Conference: Condition Assessment, Utility Engineering, Surveying, and Multidiscipline
Y2 - 12 August 2023 through 16 August 2023
ER -