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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1392-3730
1822-3605
DOI:10.3846/jcem.2022.15851