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- W108109218 abstract "Quantification of the pavement deterioration trend plays a crucial role in determining optimum pavement maintenance strategies. Nowadays, pavement distress classification and quantification becomes more important, as the flourishing artificial intelligence (AI) methods and computational power increases as well. Recently, AI methods such as neural networks (NNs), fuzzy logic (FL), and expert system (ES) provide very good analytical tools for automatic detecting and classification of pavement distresses. The main goal of hybrid method (HM) is combining the advantages of different AI methods within a single system. HM and its application toward smart management in a pavement management system is a collection of research in the fields of pavement management, pavement quantification, and distress detection and classification. It covers topics such as the combination of NNs with ESs, NNs with FL systems, and HM for the image processing of distress. This chapter deals with applications of HM in automatic pavement distress detection and classification. Furthermore, it includes recent work on multiresolution methods for detection and isolation of pavement distress such as wavelet, ridgelet, and curvelet." @default.
- W108109218 created "2016-06-24" @default.
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- W108109218 date "2013-01-01" @default.
- W108109218 modified "2023-09-23" @default.
- W108109218 title "The Hybrid Method and its Application to Smart Pavement Management" @default.
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- W108109218 doi "https://doi.org/10.1016/b978-0-12-398296-4.00019-2" @default.
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