Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan
Sediment Bypass Tunnels (SBTs) effectively mitigate reservoir sedimentation by diverting flood-laden flows, but they face significant challenges due to hydroabrasive erosion, which compromises their sustainability. Predicting this abrasion is complex due to the intricate interactions between flow hy...
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| Format: | Article |
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Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2444419 |
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| author | Ahmed Emara Sameh A. Kantoush Mohamed Saber Tetsuya Sumi Vahid Nourani Emad Mabrouk |
| author_facet | Ahmed Emara Sameh A. Kantoush Mohamed Saber Tetsuya Sumi Vahid Nourani Emad Mabrouk |
| author_sort | Ahmed Emara |
| collection | DOAJ |
| description | Sediment Bypass Tunnels (SBTs) effectively mitigate reservoir sedimentation by diverting flood-laden flows, but they face significant challenges due to hydroabrasive erosion, which compromises their sustainability. Predicting this abrasion is complex due to the intricate interactions between flow hydraulics and sediment transport, along with limited high-quality data. In this study, we explore, for the first time, the potential of using the XGBoost machine learning algorithm to predict the spatial abrasion of SBTs. The Koshibu SBT in Japan, extending approximately 4 km, was selected as the case study. Three experimental scenarios were evaluated: the entire tunnel, the straight section, and the curved section. A spatial abrasion topography was measured using laser scanning tools with a spatial resolution of 2 cm. The controlling factors for abrasion were developed based on geometric and hydraulic features. The abrasion inventory map, consisting of over 1 million data points indicating damaged and non-damaged sites, was divided equally for training and testing the XGBoost algorithm. Results indicate that the XGBoost model effectively predicts 2D spatial abrasions in SBTs, achieving an overall accuracy of 0.864, exceeding 0.9 in some sections. The developed abrasion map accurately captures various complex patterns throughout the tunnel but has some limitations in areas with small wave-like patterns. Overall, this study demonstrates the potential of machine learning algorithms for predicting tunnel abrasion in SBTs.Paper highlightsThis study introduces a validated 2D model for tunnel abrasion based on field data, contributing to improved sediment management in SBTs.ASM Model efficiently predicts abrasion mapping in SBT, achieving 86.4% overall accuracy.High sensitivity and specificity in distinguishing abraded and non-abraded areas.Captures four complex abrasion patterns in straight and curved sections but is limited to relatively small wave-like patterns.Geometric and hydraulic parameters, particularly the elongated distance and flow velocity, exhibit significant impacts in the ASM model. |
| format | Article |
| id | doaj-art-103b97b3e8b04861a23f479924de6253 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-103b97b3e8b04861a23f479924de62532024-12-26T09:38:22ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2024.2444419Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, JapanAhmed Emara0Sameh A. Kantoush1Mohamed Saber2Tetsuya Sumi3Vahid Nourani4Emad Mabrouk5Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto, JapanDisaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, JapanDisaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, JapanDisaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, JapanCenter of Excellence in Hydro Informatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranCollege of Engineering and Technology, American University of the Middle East, Egaila, KuwaitSediment Bypass Tunnels (SBTs) effectively mitigate reservoir sedimentation by diverting flood-laden flows, but they face significant challenges due to hydroabrasive erosion, which compromises their sustainability. Predicting this abrasion is complex due to the intricate interactions between flow hydraulics and sediment transport, along with limited high-quality data. In this study, we explore, for the first time, the potential of using the XGBoost machine learning algorithm to predict the spatial abrasion of SBTs. The Koshibu SBT in Japan, extending approximately 4 km, was selected as the case study. Three experimental scenarios were evaluated: the entire tunnel, the straight section, and the curved section. A spatial abrasion topography was measured using laser scanning tools with a spatial resolution of 2 cm. The controlling factors for abrasion were developed based on geometric and hydraulic features. The abrasion inventory map, consisting of over 1 million data points indicating damaged and non-damaged sites, was divided equally for training and testing the XGBoost algorithm. Results indicate that the XGBoost model effectively predicts 2D spatial abrasions in SBTs, achieving an overall accuracy of 0.864, exceeding 0.9 in some sections. The developed abrasion map accurately captures various complex patterns throughout the tunnel but has some limitations in areas with small wave-like patterns. Overall, this study demonstrates the potential of machine learning algorithms for predicting tunnel abrasion in SBTs.Paper highlightsThis study introduces a validated 2D model for tunnel abrasion based on field data, contributing to improved sediment management in SBTs.ASM Model efficiently predicts abrasion mapping in SBT, achieving 86.4% overall accuracy.High sensitivity and specificity in distinguishing abraded and non-abraded areas.Captures four complex abrasion patterns in straight and curved sections but is limited to relatively small wave-like patterns.Geometric and hydraulic parameters, particularly the elongated distance and flow velocity, exhibit significant impacts in the ASM model.https://www.tandfonline.com/doi/10.1080/19942060.2024.2444419Spatial abrasion of SBTsabrasion inventory mapXGBoost machine learningabrasion pattern predicting |
| spellingShingle | Ahmed Emara Sameh A. Kantoush Mohamed Saber Tetsuya Sumi Vahid Nourani Emad Mabrouk Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan Engineering Applications of Computational Fluid Mechanics Spatial abrasion of SBTs abrasion inventory map XGBoost machine learning abrasion pattern predicting |
| title | Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan |
| title_full | Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan |
| title_fullStr | Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan |
| title_full_unstemmed | Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan |
| title_short | Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan |
| title_sort | machine learning approach for 2d abrasion mapping in sediment bypass tunnels a case study of koshibu sbt japan |
| topic | Spatial abrasion of SBTs abrasion inventory map XGBoost machine learning abrasion pattern predicting |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2444419 |
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