Fuzzy Logic Inference Modeling (FLIM) of Culvert Design Size and Discharge Calculations

Abstract In road construction groundwater especially surface and flows play very important roles, because road paths must cross over narrow or wide drainage basins through culverts, bridges or aqueducts. Under the bridges and aqueducts water problems do not pose very dangerous situations, but culverts as comparatively small-scale engineering structures are subject to most dangerous surface water (runoff) problems and thus need more attention. Culverts under the highways and railways must be designed and constructed in such a way that they pass the upstream runoff without critical problem to downstream area. In the literature there are many reports about the water passage and dimensional design formulations for culvert works that have empirical bases and validity for the region they are planned. In most of the culvert drainage basins, there are no measurements of rainfall and therefore empirical, logical rational approaches are needed. One of such method is the bivalent logic (two-value, crisp) rule-based approach is the Talbot culvert design approach, which does not depend on complex mathematical equations and cannot consider various uncertainties (vague, imprecise, incomplete) effectively. This paper proposes fuzzy logic inference model (FLIM) approach for transformation of Talbot bivalent logic method steps into more meaningful fuzzy logic method. The application of the proposed method is made in a local area where a serious train accident occurred due to a poorly maintained culvert in Tekirdağ province near Istanbul on the European side of Turkey.

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Publication Details

Journal
Water Resources Management
Published
2025-06-17
DOI
https://doi.org/10.1007/s11269-025-04276-6
Primary Topic
Dam Engineering and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Fuzzy Logic Inference Modeling (FLIM) of Culvert Design Size and Discharge Calculations

Zekâi̇ Şen, Özay Uslu
Water Resources Management
Dam Engineering and Safety
article

Fuzzy Logic Inference Modeling (FLIM) of Culvert Design Size and Discharge Calculations

Zekâi̇ Şen, Özay Uslu
article en

Abstract

Abstract In road construction groundwater especially surface and flows play very important roles, because road paths must cross over narrow or wide drainage basins through culverts, bridges or aqueducts. Under the bridges and aqueducts water problems do not pose very dangerous situations, but culverts as comparatively small-scale engineering structures are subject to most dangerous surface water (runoff) problems and thus need more attention. Culverts under the highways and railways must be designed and constructed in such a way that they pass the upstream runoff without critical problem to downstream area. In the literature there are many reports about the water passage and dimensional design formulations for culvert works that have empirical bases and validity for the region they are planned. In most of the culvert drainage basins, there are no measurements of rainfall and therefore empirical, logical rational approaches are needed. One of such method is the bivalent logic (two-value, crisp) rule-based approach is the Talbot culvert design approach, which does not depend on complex mathematical equations and cannot consider various uncertainties (vague, imprecise, incomplete) effectively. This paper proposes fuzzy logic inference model (FLIM) approach for transformation of Talbot bivalent logic method steps into more meaningful fuzzy logic method. The application of the proposed method is made in a local area where a serious train accident occurred due to a poorly maintained culvert in Tekirdağ province near Istanbul on the European side of Turkey.

Water Resources ManagementVol. 39(13)
Istanbul Medipol University (TR)
İstanbul Medipol Üniversitesi, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Openalex Percentile: Top 85%
Dam Engineering and Safety
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Fuzzy Logic Inference Modeling (FLIM) of Culvert Design Size and Discharge Calculations — Zekâi̇ Şen, Özay Uslu · Water Resources Management (2025) | TGRS Research Map | TGRS