Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduli

Abstract

Backcalculation of pavement layer moduli refers to the process of evaluating the pavement layers using pavement surface deflections. The genetic algorithm (GA) technique was successfully used in the past for backcalculation. The BACKGA model developed by the Indian Institute of Technology, Kharagpur is one such program used for backcalculation using the GA technique. Though GA-based backcalculation models are considered to be robust due to the search algorithm adopted in the process, they require more computational time due to the large number of times the surface deflections are computed using different sets of layer moduli. In the present work, artificial neural network (ANN) models have been developed for computing surface deflections using elastic moduli and thicknesses of pavement layers as inputs. The ANN models have been used in BACKGA for forward calculation of surface deflections to combine the computational efficiency of ANNs with the robustness of the GAs. The performance of the resulting model, BACKGA–ANN, has been evaluated and found to be satisfactory.

Keywords:

• FWD
• Surface deflections
• Layer moduli
• Backcalculation
• Artificial neural networks
• Genetic algorithms

Notes

^¶Email: [email protected]

^§Email: [email protected] [email protected]

Additional information

Notes on contributors

Nune Rakesh

¶ ¶Email: [email protected]

K. Sudhakar Reddy

§ §Email: [email protected] [email protected]