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    Automated Detection and Classification of Infiltration in Sewer Pipes 

    Source: Journal of Infrastructure Systems:;2005:;Volume ( 011 ):;issue: 003
    Author(s): Tariq Shehab; Osama Moselhi
    Publisher: American Society of Civil Engineers
    Abstract: Many researchers have reported on the poor status of sewer pipes in Canadian cities, revealing the presence of a number of defects that impact their performance. Inadequate inspection is considered one of the main causes ...
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    Classification of Defects in Sewer Pipes Using Neural Networks 

    Source: Journal of Infrastructure Systems:;2000:;Volume ( 006 ):;issue: 003
    Author(s): Osama Moselhi; Tariq Shehab-Eldeen
    Publisher: American Society of Civil Engineers
    Abstract: Deterioration of underground infrastructure facilities such as sewer pipes poses a serious problem to most developed urban centers today. As distribution piping networks age, they deteriorate and may ultimately fail to ...
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    Cost-Estimating Model for Rubberized Asphalt Pavement Rehabilitation Projects 

    Source: Journal of Infrastructure Systems:;2013:;Volume ( 019 ):;issue: 004
    Author(s): Tariq Shehab; Ida Meisami-Fard
    Publisher: American Society of Civil Engineers
    Abstract: There are nearly 4,800,000 km (3,000,000 miles) of paved roads and 80,000 km (50,000 miles) of paved highways in the United States, many of which are in poor condition and approaching the end of their design life. To upgrade ...
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    Cost Estimating Models for Utility Rehabilitation Projects: Neural Networks versus Regression 

    Source: Journal of Pipeline Systems Engineering and Practice:;2010:;Volume ( 001 ):;issue: 003
    Author(s): Tariq Shehab; Mohamad Farooq; Suprea Sandhu; Tang-Hung Nguyen; Elhami Nasr
    Publisher: American Society of Civil Engineers
    Abstract: Due to the poor condition of the sewer and water networks in many communities across the United States, many rehabilitation projects are being undertaken to improve their condition. With limited availability of funds, early ...
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