Condition monitoring and temporal‐spatial assessment of composite pipeline transporting potable water

Wang, Jun‐Fang; Zhang, Linhao; Lin, J.; Wang, Baoxian; Ni, Yiqing; Ren, Wei‐Xin; Wang, You‐Wu; Xie, Yan‐Long

doi:10.1002/stc.3020

Cited by 2 publications

(1 citation statement)

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“…Te goal is to utilize the internal logical relationships between numerous independent variables (i.e., various infuencing factors) and dependent variables (i.e., the degree of deterioration and damage of pipelines) to predict potential damage levels. Among the artifcial intelligence techniques propelled by advanced intelligence, artifcial neural networks, fuzzy logic, and a variety of machine learning algorithms [98] have proven to be more mature and widely used in the operation and maintenance of drainage pipelines. Figure 10 illustrates their operational mechanisms and the pipeline damage prediction and evaluation process.…”

Section: Damage Prediction and Operation And Maintenance Ofmentioning

confidence: 99%

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Luo,

Du,

Niu

2024

Structural Control and Health Monitoring

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During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.

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Section: Damage Prediction and Operation And Maintenance Ofmentioning

confidence: 99%