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    Novel System for Rapid Investigation and Damage Detection in Cultural Heritage Conservation Based on Deep Learning 

    Source: Journal of Infrastructure Systems:;2019:;Volume ( 025 ):;issue: 003
    Author(s): Niannian Wang; Xuefeng Zhao; Linan Wang; Zheng Zou
    Publisher: American Society of Civil Engineers
    Abstract: Rapid investigation and damage assessment are crucial for cultural heritage conservation. At present, mobile crowd sensing (MCS) techniques are very effective for cultural heritage investigation and data collection. ...
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    Leveraging Machine Learning for Pipeline Condition Assessment 

    Source: Journal of Pipeline Systems Engineering and Practice:;2023:;Volume ( 014 ):;issue: 003:;page 04023024-1
    Author(s): Hongfang Lu; Zhao-Dong Xu; Xulei Zang; Dongmin Xi; Tom Iseley; John C. Matthews; Niannian Wang
    Publisher: ASCE
    Abstract: Pipeline condition assessment is a cost-effective method to determine the status of pipeline structure and predict failure probability. Although 100% inspection may not be feasible for decision makers, recent advancements ...
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    A Feature Selection–Based Intelligent Framework for Predicting Maximum Depth of Corroded Pipeline Defects 

    Source: Journal of Performance of Constructed Facilities:;2022:;Volume ( 036 ):;issue: 005:;page 04022044
    Author(s): Hongfang Lu; Haoyan Peng; Zhao-Dong Xu; John C. Matthews; Niannian Wang; Tom Iseley
    Publisher: ASCE
    Abstract: Corrosion is one of the most common defects of buried pipelines. Accurate prediction of the maximum pitting depth of corroded pipelines is conducive to assessing the remaining strength of the pipeline. In the context of ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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