In recent years, such nature-inspired metaheuristics are emerging as successful alternatives to more classical approaches for solving optimization problems that contain uncertainty, stochasticity, and dynamic information in their mathematical formulation. Similarly, the ant colony optimization (ACO) approach imitates the real-world foraging behavior shown by ants when they search for food and particle swarm optimization (PSO) is inspired by social behavior of bird flocking or fish schooling. For instance, one of the most well-established nature-inspired heuristic techniques is the genetic algorithm (GA), which is based on the survival-of-the-fittest notion espoused by Darwin’s theory of evolution. This paper describes how biologically inspired computing or natural computing is a field of research that takes inspiration from nature, biology, physical systems, and the social behavior of natural systems for developing computational techniques to solve complex optimization problems. Therefore, the assumption in FAARFIELD that the 25 percent reduction on edge stress accounting for the load transfer may not be suitable under some circumstance.Backcalculation of Pavement Moduli Using Bio-Inspired Hybrid Metaheuristics and Cooperative Strategies Thirdly, the value of ratio between the critical stress of 9-slab and the 1-slab pavement system (S9/S1) varied differently to different scenarios. Secondly, the combination of temperature gradient and the thickness of the slab predominantly influenced the critical tensile stress and the stress-based LTE of the slab. Firstly, the critical stress location for the slab loaded at the corner was more sensitive to different scenarios than those at the edge. Moreover, the FEAFAA results demonstrated certain results.
Thirdly, the overlay thickness calculated by different methods was clearly dissimilar to each other.
The backcalculated modulus of subgrade reaction from both methods was significantly greater than lab test data because they were assumed as a two-layered system in which the property of lower layer represented both the base and the subgrade layers. Secondly, the backcalculated elastic modulus obtained by the AREA method was closely matched to the lab test data whereas the NUS-BACK seemed to be overestimated. Firstly, the deflection-based LTE was found sensitive to several factors including the assessed position, the amount of load level, test direction, and the adjacent support of the evaluated slab. The analyzed data from HWD test illustrated several findings. In the second part, Finite Element Analysis Federal Aviation Administration (FEAFAA) was selected as a tool to investigate the stress-based joint load transfer efficiency under various input scenarios including variations in the temperature gradients of slab, landing gear configurations, traffic directions, and slab thicknesses. Those layer moduli were then applied as the input parameters for the overlay design using Federal Aviation Administration Rigid and Flexible Iterative Elastic Layered Design (FAARFIELD) to analyze their influences on the designed overlay thickness. Subsequently, the backcalculated layer moduli were compared with the lab test data.
Initially, the sensitivities of the deflection-based load transfer efficiency (LTE) were evaluated. The input condition taken into the backcalculation was a field data of Heavy Weight Deflectometer (HWD) round-up project in the National Airport Pavement Test Facility (NAPT) in Atlantic City, NJ. In the first part, the AREA method and the Graphical NUS-BACK solution were primary backcalculation methods. The analyses were designed comprising two main methods. Description This study was aimed to analyze the sensitivity of the backcalculation of layer moduli and the joint load transfer efficiency of airfield rigid pavement.