Evaluation of eco-friendly soil slope stabilization techniques for forest roads by using an Artificial Neural Network (ANN)

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dc.authorid0000-0001-9660-5028
dc.authorid0000-0001-6558-9029
dc.contributor.authorYuksel, Kivanc
dc.contributor.authorGulci, Nese
dc.contributor.authorAkay, Abdullah Emin
dc.contributor.authorGulci, Sercan
dc.date.accessioned2026-02-08T15:15:19Z
dc.date.available2026-02-08T15:15:19Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, the effectiveness of different stabilization techniques implemented on the forest road cut slopes was investigated in terms of controlling erosion and runoff. Wood production residues, hydro-seeding, and jute geotextile treatments were applied on study plots located on the example road. The amount of erosion and runoff were measured on the study plots which were established for different slope grades of 20 degrees, 30 degrees, and 40 degrees. Then, the amount of erosion and runoff measured from the plots were compared to determine the performance of stabilization techniques on the cut slope. In the solution process, an Artificial Neural Network (ANN) model, which is one of the machine learning algorithms, was used to predict sediment yield from forest road cut slopes. The sediment yields averaged over the three slope grades from highest to lowest were measured as 6.41, 1.16, 0.65, and 0.45 g/m2 in the control plot with no treatment, jute geotextile, hydroseeding, and wood production residues, respectively. The averaged over the three runoff amounts slope grades from the highest to the lowest were determined as 6.82, 3.71, 1.64, and 1.30 mm/m2 in control the plot, jute geotextile, hydroseeding, and wood production residues, respectively. Comparing to the control plot, wood production residues, hydroseeding, and jute geotextile treatments reduced the sediment yields by 14,10, and 5 times, respectively. On the other hand, wood production residues, hydroseeding, and jute geotextile applications reduced the runoff amount by 5, 4, and 2 times, respectively. As a result, it was found that wood production residues and hydroseeding treatment can be more efficient in reducing the amount of runoff and sediment yield compared to the jute geotextile treatment. The ANN method achieved high accuracy in predicting sediment yield and it was concluded that the ANN can be used as an effective method to evaluate soil slope stabilization techniques. (c) 2025 International Research and Training Centre on Erosion and Sedimentation. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.description.sponsorshipKahramanmaras Sutcu Imam University, Division of Scientific Research Projects [2019/1-20 D]
dc.description.sponsorshipThis paper presents a part of the Ph.D. dissertation (Yueksel, 2022) conducted by K & imath;vanc Yueksel (2016-2022) under the super-vision of Asst. Prof. Nese Guelci in the Forest Engineering Department, Institute of Applied Sciences, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey. This study was funded by Kahramanmaras Sutcu Imam University, Division of Scientific Research Projects (Grant No. 2019/1-20 D) . The authors would like to thank Dr. Buelent Abis for his help with the field work.
dc.identifier.doi10.1016/j.ijsrc.2025.01.011
dc.identifier.endpage488
dc.identifier.issn1001-6279
dc.identifier.issn2589-7284
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85219580902
dc.identifier.scopusqualityQ1
dc.identifier.startpage476
dc.identifier.urihttps://doi.org/10.1016/j.ijsrc.2025.01.011
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5705
dc.identifier.volume40
dc.identifier.wosWOS:001515024600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKeai Publishing Ltd
dc.relation.ispartofInternational Journal of Sediment Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectForest roads
dc.subjectSoil slope stabilization
dc.subjectSediment yield
dc.subjectRunoff
dc.subjectCut slope
dc.subjectArtificial Neural Network (ANN)
dc.titleEvaluation of eco-friendly soil slope stabilization techniques for forest roads by using an Artificial Neural Network (ANN)
dc.typeArticle

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