Neural Network–Swarm Intelligence Hybrid Nonlinear Optimization Algorithm for Pavement Moduli Back-Calculation

Abstract

This paper describes a novel hybrid intelligent system approach to inversion of nondestructive pavement deflection data and back-calculation of nonlinear stress-dependent pavement layer moduli. Particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling, is fast emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business, and finance. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. PSO is especially considered a robust and efficient approach for global optimization of multimodal functions. The hybrid back-calculation system described in this paper integrates finite element modeling, neural networks, and PSO in an efficient manner to mitigate the limitations and take advantages of the strengths to produce a system that is more effective and powerful than those which could be built with single technique. This is the first time the PSO approach is applied to real-time nondestructive evaluation of pavement systems.