6533b86efe1ef96bd12cbf4a
RESEARCH PRODUCT
Optimizing artificial neural networks for the evaluation of asphalt pavement structural performance
Gaetano BosurgiOrazio PellegrinoGiuseppe Sollazzosubject
lcsh:TE1-450Computer science0211 other engineering and technologies020101 civil engineering02 engineering and technology0201 civil engineeringlcsh:TG1-470lcsh:Bridge engineeringAsphalt pavementDeflection (engineering)021105 building & constructionSettore ICAR/04 - Strade Ferrovie Ed AeroportiAsphalt pavementArchitectureArtificial Neural Network (ANN); asphalt pavement; Long Term Pavement Performance (LTPP); neural network optimisation; Pavement Management System (PMS); structural performancelcsh:Highway engineering. Roads and pavementsCivil and Structural EngineeringArtificial neural network (ANN)Network architectureTraining setArtificial neural networkPavement managementBuilding and ConstructionPavement management system (PMS)Structural performanceReliability engineeringNeural network optimisationAsphaltLong term pavement performance (LTPP)Artificial neural network (ANN) Asphalt pavement Long term pavement performance (LTPP) Neural network optimisation Pavement management system (PMS) Structural performancedescription
Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road agencies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), “homogeneity” of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-03-28 |