Sewer condition prediction is a fundamental element of proactive maintenance programs. The prediction relies mostly on the assessed condition of inspected segments, generally based on CCTV reports. However, several sources of uncertainty affect the condition assessment and may lead to inefficient maintenance. The present article focuses on three main questions. 1. What is the impact of uncertainty in assessed condition on the prediction model? 2. Considering uncertainties in the assessed condition, is it necessary to collect data on the characteristics of many segments, or are a small number of influential variables enough to build the condition prediction model? 3. Is it better to overestimate (false positive) or underestimate (false negative) the deterioration of a segment? These questions were evaluated on a semi-virtual asset stock and the results confirm that uncertainties affect the inspection efficiency negatively. Results also show that errors leading to the overestimation of the deterioration have less negative impact. The study suggests that data from a small number of influential segments is adequate to inform the prediction model.
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Kompetenzzentrum Wasser Berlin, Berlin, Germany
Univ Claude Bernard Lyon 1, INSA LYON, DEEP, Villeurbanne, FranceKompetenzzentrum Wasser Berlin, Berlin, Germany
Caradot, Nicolas
Riechel, Mathias
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Kompetenzzentrum Wasser Berlin, Berlin, GermanyKompetenzzentrum Wasser Berlin, Berlin, Germany
Riechel, Mathias
Rouault, Pascale
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Kompetenzzentrum Wasser Berlin, Berlin, GermanyKompetenzzentrum Wasser Berlin, Berlin, Germany
Rouault, Pascale
Caradot, Antoine
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Univ Claude Bernard Lyon 1, Inst Camille Jordan, Villeurbanne, FranceKompetenzzentrum Wasser Berlin, Berlin, Germany
Caradot, Antoine
Lengemann, Nic
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Berliner Wasserbetriebe, Berlin, GermanyKompetenzzentrum Wasser Berlin, Berlin, Germany
Lengemann, Nic
Eckert, Elke
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Berliner Wasserbetriebe, Berlin, GermanyKompetenzzentrum Wasser Berlin, Berlin, Germany
Eckert, Elke
Ringe, Alexander
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Berliner Wasserbetriebe, Berlin, GermanyKompetenzzentrum Wasser Berlin, Berlin, Germany
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Sejong Univ, Dept Comp Sci & Engn, Seoul 05006, South KoreaSejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
Li, Yanfen
Wang, Hanxiang
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Sejong Univ, Dept Comp Sci & Engn, Seoul 05006, South KoreaSejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
Wang, Hanxiang
Dang, L. Minh
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Sejong Univ, Dept Informat & Commun Engn & Convergence Engn In, Seoul 05006, South KoreaSejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
Dang, L. Minh
Song, Hyoung-Kyu
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Sejong Univ, Dept Informat & Commun Engn & Convergence Engn In, Seoul 05006, South KoreaSejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
Song, Hyoung-Kyu
Moon, Hyeonjoon
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Sejong Univ, Dept Comp Sci & Engn, Seoul 05006, South KoreaSejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
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Delft Univ Technol, POB 5048, NL-2600 GA Delft, NetherlandsDelft Univ Technol, POB 5048, NL-2600 GA Delft, Netherlands
Lepot, Mathieu
Stanic, Nikola
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Delft Univ Technol, POB 5048, NL-2600 GA Delft, NetherlandsDelft Univ Technol, POB 5048, NL-2600 GA Delft, Netherlands
Stanic, Nikola
Clemens, Francois H. L. R.
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Delft Univ Technol, POB 5048, NL-2600 GA Delft, Netherlands
Deltares, POB 177, NL-2600 MH Delft, NetherlandsDelft Univ Technol, POB 5048, NL-2600 GA Delft, Netherlands