Modeling of pavement roughness utilizing artificial neural network approach for Laos national road network

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 secti...

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Veröffentlicht in:	Journal of civil engineering and management Jg. 28; H. 4; S. 261 - 277
Hauptverfasser:	Gharieb, Mohamed, Nishikawa, Takafumi, Nakamura, Shozo, Thepvongsa, Khampaseuth
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vilnius Vilnius Gediminas Technical University 08.03.2022
Schlagworte:	artificial neural network (ann) Artificial neural networks Asphalt asphalt concrete (ac) backpropagation algorithm Computer programs cumulative equivalent single-axle load (cesal) Data base management systems Datasets double bituminous surface treatment (dbst) Goodness of fit international roughness index (iri) laos pavement management system (pms) Model accuracy Neural networks pavement age Pavement management pavement performance model Pavements Regression analysis Roads & highways Roughness Statistical analysis Surface treatment Training Laos International Roughness Index (IRI) artificial neural network (ANN) Laos pavement management system (PMS) backpropagation algorithm cumulative equivalent single-axle load (CESAL) double bituminous surface treatment (DBST) pavement performance model asphalt concrete (AC) pavement age
ISSN:	1392-3730, 1822-3605
Online-Zugang:	Volltext
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