{"created":"2023-05-15T16:49:46.732876+00:00","id":27375,"links":{},"metadata":{"_buckets":{"deposit":"6e0ece39-42fa-4bd7-8552-52bd66fef745"},"_deposit":{"created_by":6,"id":"27375","owners":[6],"pid":{"revision_id":0,"type":"depid","value":"27375"},"status":"published"},"_oai":{"id":"oai:nagasaki-u.repo.nii.ac.jp:00027375","sets":["14:21"]},"author_link":["121791","121792","121793","121790"],"item_2_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"277","bibliographicPageStart":"261","bibliographicVolumeNumber":"28","bibliographic_titles":[{"bibliographic_title":"Journal of Civil Engineering and Management"}]}]},"item_2_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Abstract"}]},"item_2_description_63":{"attribute_name":"引用","attribute_value_mlt":[{"subitem_description":"Journal of Civil Engineering and Management, 28(4), pp261-277; 2022","subitem_description_type":"Other"}]},"item_2_publisher_33":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Vilnius Gediminas Technical University"}]},"item_2_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.3846/jcem.2022.15851","subitem_relation_type_select":"DOI"}}]},"item_2_rights_13":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":" © 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."}]},"item_2_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1392-3730","subitem_source_identifier_type":"ISSN"}]},"item_2_source_id_8":{"attribute_name":"EISSN","attribute_value_mlt":[{"subitem_source_identifier":"1822-3605","subitem_source_identifier_type":"ISSN"}]},"item_2_version_type_16":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"GHARIEB, Mohamed"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"NISHIKAWA, Takafumi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"NAKAMURA, Shozo"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"THEPVONGSA, Khampaseuth"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-05-13"}],"displaytype":"detail","filename":"JCEM28_261.pdf","filesize":[{"value":"1.8 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"JCEM28_261.pdf","url":"https://nagasaki-u.repo.nii.ac.jp/record/27375/files/JCEM28_261.pdf"},"version_id":"82dd7770-d469-41b2-a94b-1995bd0a95cb"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"International Roughness Index (IRI)","subitem_subject_scheme":"Other"},{"subitem_subject":"Laos pavement management system (PMS)","subitem_subject_scheme":"Other"},{"subitem_subject":"artificial neural network (ANN)","subitem_subject_scheme":"Other"},{"subitem_subject":"backpropagation algorithm","subitem_subject_scheme":"Other"},{"subitem_subject":"double bituminous surface treatment (DBST)","subitem_subject_scheme":"Other"},{"subitem_subject":"asphalt concrete (AC)","subitem_subject_scheme":"Other"},{"subitem_subject":"pavement age","subitem_subject_scheme":"Other"},{"subitem_subject":"cumulative equivalent single-axle load (CESAL)","subitem_subject_scheme":"Other"},{"subitem_subject":"pavement performance model","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK"}]},"item_type_id":"2","owner":"6","path":["21"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-13"},"publish_date":"2022-05-13","publish_status":"0","recid":"27375","relation_version_is_last":true,"title":["MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK"],"weko_creator_id":"6","weko_shared_id":-1},"updated":"2023-05-15T19:48:26.018047+00:00"}