Document Type
Article
Publication Date
1-1-2017
Abstract
The subgrade resilient modulus is an important parameter in pavement analysis and design. However, available non-destructive testing devices such as the falling weight deflectometer (FWD) have limitations that prevent their widespread use at the network level. This study describes the development of a model that utilizes the rolling wheel deflectometer (RWD) measurements to predict the subgrade resilient modulus at the network level for flexible pavements. Measurements ofRWDand FWD obtained from a testing program conducted in Louisiana were used to train an artificial neural network (ANN) based model. The ANN model was validated using data from a testing program independently conducted in Minnesota. The ANN model showed acceptable accuracy in both the development and validation phases with coefficients of determination of 0.73 and 0.72, respectively. Furthermore, the limits of agreement methodology showed that 95% of the differences between the subgrade resilient modulus calculated based on FWD and RWD measurements will not exceed the range of ±21 MPa (±3 ksi).
Publication Source (Journal or Book title)
Canadian Journal of Civil Engineering
First Page
700
Last Page
706
Recommended Citation
Elbagalati, O., Elseifi, M., Gaspard, K., & Zhang, Z. (2017). Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing. Canadian Journal of Civil Engineering, 44 (9), 700-706. https://doi.org/10.1139/cjce-2017-0132