Master of Science / Department of Civil Engineering / Mustaque A. Hossain / The objective of this research project was to assess the condition of general aviation airport pavements in Kansas. The study was also intended to form the basis for a pavement management system (PMS). A total of 137 runways from 107 airports across the state were surveyed. MicroPAVER, a PMS system developed by the U.S. Army Corps of Engineers, was selected as the platform for the PMS. An inventory database was developed for all runways in the network. Information about the construction and maintenance history was entered into the MicroPAVER database. On-site surveys were conducted between the months of May and July of 2008 to assess pavement conditions in terms of the Pavement Condition Index (PCI), following the methodology outlined by ASTM D 5340-04 and adopted by the Federal Aviation Administration (FAA). Approximately 68% of the sections surveyed were in “good” to “satisfactory” condition. Almost one-third of the network can be rated as “good.” About 21% of the sections studied were in “fair” condition. Overall, the condition of the network can be rated as “satisfactory.” A condition prediction curve was developed for each of the two different types of surfaces. From the prediction curves created using MicroPAVER, it was estimated that the number of branches rated as “good” could decrease by 50% by 2010. As much as 44% of the network could have a rating of “fair” by 2013 if the sections receive only routine maintenance. Two budget scenario comparison reports developed show that the 108 runways of the 78 general aviation airports eligible for FAA funding in Kansas could be brought to a “satisfactory” rating or above (i.e. average PCI ≥ 70) by spending approximately $15 million on average per year for the next five years.

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Infrastructure systems are crucial facilities. They supply the necessary transportation, water and energy utilities for the public. However, while aging, these systems gradually deteriorate in time and approach the end of their lifespans. As a result, they require periodic maintenance and repair in order to function and be reliable throughout their lifetimes. Bridge infrastructure is an essential part of the transportation infrastructure. Bridge management systems (BMSs), used to monitor the condition and safety of the bridges in a bridge infrastructure, have evolved considerably in the past decades. The aim of BMSs is to use the resources in an optimal manner keeping the bridges out of risk of failure. The BMSs use the lifetime performance curves to predict the future condition of the bridge elements or bridges. The most widely implemented condition-based performance prediction and maintenance optimization model is the Markov Decision Process-based models (MDP). The importance of the Markov Decision Process-based model is that it defines the time-variant deterioration using the Markov Transition Probability Matrix and performs the lifetime cost optimization by finding the optimum maintenance policy. In this study, the Markov decision process-based model is examined and a computer program to find the optimal policy with discounted life-cycle cost is developed. The other performance prediction model investigated in this study is a probabilistic Bi-linear model which takes into account the uncertainties for the deterioration process and the application of maintenance actions by the use of random variables. As part of the study, in order to further analyze and develop the Bi-linear model, a Latin Hypercube Sampling-based (LHS) simulation program is also developed and integrated into the main computational algorithm which can produce condition, safety, and life-cycle cost profiles for bridge members with and without maintenance actions. Furthermore, a polynomial-based condition prediction is also examined as an alternative performance prediction model. This model is obtained from condition rating data by applying regression analysis. Regression-based performance curves are regenerated using the Latin Hypercube sampling method. Finally, the results from the Markov chain-based performance prediction are compared with Simulation-based Bi-linear prediction and the derivation of the transition probability matrix from simulated regression based condition profile is introduced as a newly developed approach. It has been observed that the results obtained from the Markov chain-based average condition rating profiles match well with those obtained from Simulation-based mean condition rating profiles. The result suggests that the Simulation-based condition prediction model may be considered as a potential model in future BMSs.

Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.

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