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MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK
http://hdl.handle.net/10069/00041513
http://hdl.handle.net/10069/0004151302d7e59e-bb2f-43ba-aebe-b14fd289e647
名前 / ファイル | ライセンス | アクション |
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JCEM28_261.pdf (1.8 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-05-13 | |||||
タイトル | ||||||
タイトル | MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | International Roughness Index (IRI) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Laos pavement management system (PMS) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | artificial neural network (ANN) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | backpropagation algorithm | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | double bituminous surface treatment (DBST) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | asphalt concrete (AC) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | pavement age | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | cumulative equivalent single-axle load (CESAL) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | pavement performance model | |||||
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 | The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models. | |||||
書誌情報 |
Journal of Civil Engineering and Management 巻 28, 号 4, p. 261-277, 発行日 2022-03-08 |
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出版者 | ||||||
出版者 | Vilnius Gediminas Technical University | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1392-3730 | |||||
EISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1822-3605 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.3846/jcem.2022.15851 | |||||
権利 | ||||||
権利情報 | © 2022 The Author(s). Published by Vilnius Gediminas Technical University This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |||||
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 | Journal of Civil Engineering and Management, 28(4), pp261-277; 2022 |