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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Vydáno v:	Journal of civil engineering and management Ročník 28; číslo 4; s. 261 - 277
Hlavní autoři:	Gharieb, Mohamed, Nishikawa, Takafumi, Nakamura, Shozo, Thepvongsa, Khampaseuth
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vilnius Vilnius Gediminas Technical University 08.03.2022
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1392-3730 1822-3605
DOI:	10.3846/jcem.2022.15851