%0 Journal Article
%T Assessing artificial neural network performance in estimating the layer properties of pavements
%A Gloria In¨¦s Beltran
%A Miguel Pedro Romo
%J -
%D 2014
%X A major concern in assessing the structural condition of existing flexible pavements is the estimation of the mechanical properties of constituent layers, which is useful for the design and decision-making process in road management systems. This parameter identification problem is truly complex due to the large number of variables involved in pavement behavior. To this end, non-conventional adaptive or approximate solutions via Artificial Neural Networks ¨C ANNs ¨C are considered to properly map pavement response field measurements. Previous investigations have demonstrated the exceptional ability of ANNs in layer moduli estimation from non-destructive deflection tests, but most of the reported cases were developed using synthetic deflection data or hypothetical pavement systems. This paper presents further attempts to back-calculate layer moduli via ANN modeling, using a database gathered from field tests performed on three- and four-layer pavement systems. Traditional layer structuring and pavements with a stabilized subbase were considered. A three-stage methodology is developed in this study to design and validate an ˇ°optimumˇ± ANN-based model, i.e., the best architecture possible along with adequate learning rules. An assessment of the resulting ANN model demonstrates its forecasting capabilities and efficiency in solving a complex parameter identification problem concerning pavements
%K Artificial neural networks
%K pavements
%K non-destructive testing
%K deflections
%K layer moduli
%U https://revistas.unal.edu.co/index.php/ingeinv/article/view/42158