A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing
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 of RWD and 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).
Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing
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 of RWD and 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).
Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing
Elseifi, Mostafa A (author) / Gaspard, Kevin / Zhang, Zhongjie / Elbagalati, Omar
2017
Article (Journal)
English
| British Library Online Contents | 2017
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