The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.

A three Dimensional finite element model (FEM) incorporating the anisotropic properties and temperature profile of hot mix asphalt (HMA) pavement was developed to predict the structural responses of HMA pavement subject to heavy loads typically encountered in the field. In this study, ABAQUS was adopted to model the stress and strain relationships within the pavement structure. The results of the model were verified using data collected from the Korean Highway Corporation Test Road (KHCTR). The results demonstrated that both the base course and surface course layers follow the anisotropic behavior and the incorporation of the temperature profile throughout the pavement has a substantial effect on the pavement response predictions that impact pavement design. The results also showed that the anisotropy level of HMA and base material can be reduced to as low as 80% and 15% as a result of repeated loading, respectively.