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Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements

  • A. Öcal
  • O. PekcanEmail author
Chapter
  • 3 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA.

Keywords

Cuckoo search Optimization Metaheuristics Artificial neural networks Backcalculation Flexible pavements 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Civil Engineering DepartmentÇankaya UniversityAnkaraTurkey
  2. 2.Civil Engineering DepartmentMiddle East Technical UniversityAnkaraTurkey

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