Bio-inspired approaches for critical infrastructure protection: Application of clonal selection principle for intrusion detection and FACTS placement

In this research, Clonal Selection, an immune system inspired approach, is utilized along with Evolutionary Algorithms to solve complex engineering problems such as Intrusion Detection and optimization of Flexible AC Transmission System (FACTS) device placement in a power grid. The clonal selection principle increases the strength of good solutions and alters their properties to find better solutions in a problem space. A special class of evolutionary algorithms that utilizes the clonal selection principle to guide its heuristic search process is termed Clonal EA. Clonal EAs can be used to solve complex pattern recognition and function optimization problems, which involve searching an enormous problem space for a solution. Intrusion Detection is modeled, in this research, as a pattern recognition problem wherein efficient detectors are to be designed to detect intrusive behavior. Optimization of FACTS device placement in a power grid is modeled as a function optimization problem wherein optimal placement positions for FACTS devices are to be determined, in order to balance load across power lines. Clonal EAs are designed to implement the solution models. The benefits and limitations of using Clonal EAs to solve the above mentioned problems are discussed and the performance of Clonal EAs is compared with that of traditional evolutionary algorithms and greedy algorithms --Abstract, page iii

Investigation of AASHTOWare Pavement ME Design/DARWin-MEPerformance Prediction Models for Iowa Pavement Analysis and Design

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG was subsequently supported by AASHTO’s DARWin-ME and most recently marketed as AASHTOWare Pavement ME Design software as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models have been implemented along with other documented changes as the MEPDG transitioned to AASHTOWare Pavement ME Design software. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1), and DARWin-ME (version 1.1) performance predictions for new jointed plain concrete pavement (JPCP), new hot mix asphalt (HMA), and HMA over JPCP systems. Differences were indeed observed between the pavement performance predictions produced by these different software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASHTOWare Pavement ME Design at the time this research was conducted. Therefore, the primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously identified MEPDG calibration factors (through InTrans Project 11-401) and, if needed, refine the local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA, and HMA over JPCP sections were used. The local calibration results of AASHTOWare Pavement ME Design are presented and compared with national and locally calibrated MEPDG models

Adaptive neuro-fuzzy inference system-based backcalculation approach to airport pavement structural analysis

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) methodology for the backcalculation of airport flexible pavement layer moduli. The proposed ANFIS-based backcalculation approach employs a hybrid learning procedure to construct a non-linear input-output mapping based on qualitative aspects of human knowledge and pavement engineering experience incorporated in the form of fuzzy if-then rules as well as synthetically generated Finite Element (FE) based pavement modeling solutions in the form of input-output data pairs. The developed neuro-fuzzy backcalculation methodology was evaluated using hypothetical data as well as extensive non-destructive field deflection data acquired from a state-of-the-art full-scale airport pavement test facility. It was shown that the ANFIS based backcalculation approach inherits the fundamental capability of a fuzzy model to especially deal with nonrandom uncertainties, vagueness, and imprecision associated with non-linear inverse analysis of transient pavement surface deflection measurements