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Application of Adaptive Neuro-Fuzzy Inference System for Forecasting Pavement Roughness in Laos
http://hdl.handle.net/10069/00041398
http://hdl.handle.net/10069/00041398d044c865-055c-462b-94c0-d502dc6283d2
名前 / ファイル | ライセンス | アクション |
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Coatings12-380.pdf (8.4 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-04-18 | |||||
タイトル | ||||||
タイトル | Application of Adaptive Neuro-Fuzzy Inference System for Forecasting Pavement Roughness in Laos | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | IRI | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | PMS | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | ANFIS | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Gharieb, Mohamed
× Gharieb, Mohamed× Nishikawa, Takafumi× Nakamura, Shozo× Thepvongsa, Khampaseuth |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions. |
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書誌情報 |
Coatings 巻 12, 号 3, p. 380, 発行日 2022-03-14 |
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出版者 | ||||||
出版者 | MDPI | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.3390/coatings12030380 | |||||
権利 | ||||||
権利情報 | ©2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Coatings, 12(3), art.no.380; 2022 |