Abstract
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 – ANNs – 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 “optimum” 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.
Highlights
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
Flexible pavements are considered as multilayer systems under repeated loading, whose structural response significantly depends on the features of the pavement layers: materials, stiffness, strength and thickness
The performance of ANN modeling in estimating the layer moduli of pavements was assessed in terms of forecasting reliability and efficiency
Summary
Impulse load tests using FWD and HWD devices properly simulate traffic loading features such as type, magnitude and timevarying vehicle loading The scope and limitations of this testing procedure should be considered in a proper interpretation of the deflection data: all parameters derived represent the loading and environmental conditions at the time of testing It should be borne in mind that most analytical methods are not suitable when either extensive deterioration or thin layers exist in the pavement (ASTM, 2003)
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