![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
In this paper, a hybrid neural network (NN)-genetic algorithm (GA) based non-destructive pavement auscultation method for instantaneous airfield infrastructure condition assessment is discussed. NNs are employed for finite element aided forward prediction of pavement surface deflections resulting from non-destructive test impulse loading and the GAs are used for global optimisation of the pavement structural parameters by matching the NN predicted deflections with the measured pavement response. This hybrid approach takes advantage of the non-linear estimation capability provided by neural networks trained using finite element (FE) solutions in modelling the stress-dependent behaviour of unbound pavement geo-materials while improving the robustness to measurement uncertainty through the application of genetic algorithms. The performance of the developed hybrid pavement auscultation technique is evaluated through extensive field studies conducted at a state-of-the-art full-scale airfield pavement test facility. The results show that this approach is promising for real-time condition evaluation of airfield pavement infrastructure systems.
Acknowledgments/Disclaimer
The author gratefully acknowledges the FAA Airport Technology Research and Development Branch for providing the data referenced in this paper. Dr Patricia Watts is the FAA Program Manager for Air Transportation Centers of Excellence and Dr Satish Agrawal is the Manager of the FAA Airport Technology R & D Branch. The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented within. The contents do not necessarily reflect the official views and policies of the Federal Aviation Administration. This paper does not constitute a standard, specification, or regulation.