One of today's most serious social, economical and environmental problems is traffic congestion. In addition to the financial cost of the problem, the number of traffic related injuries and casualties is very high. A recently considered approach to increase safety while reducing congestion and improving driving conditions is Automated Highway Systems (AHS). The AHS will evolve from the present highway system to an intelligent vehicle/highway system that will incorporate communication, vehicle control and traffic management techniques to provide safe, fast and more efficient surface transportation. A key factor in AHS deployment is intelligent vehicle control. While the technology to safely maneuver the vehicles exists, the problem of making intelligent decisions to improve a single vehicle's travel time and safety while optimizing the overall traffic flow is still a stumbling block. We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of adapting to the automata environment resulting from unmodeled physical environment. Simulations for simultaneous lateral and longitudinal control of an autonomous vehicle provide encouraging results. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is also possible by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is then carried onto the interaction of multiple intelligent vehicles. By analyzing the situations consisting of conflicting desired vehicle paths, we extend our design by additional decision structures. The analysis of the situations and the design of the additional structures are made possible by the study of the interacting reward-penalty mechanisms in individual vehicles. The definition of the physical environment of a vehicle as a series of discrete state transitions associated with a "stationary automata environment" is the key to this analysis and to the design of the intelligent vehicle path controller. This work was supported in part by the Center for Transportation Research and Virginia DOT under Smart Road project, by General Motors ITS Fellowship program, and by Naval Research Laboratory under grant no. N000114-93-1-G022. / Ph. D.

The use of cold-formed steel (CFS) structures has become increasingly popular in different fields of building technology. For example, small housing systems using cold-formed steel for wall structures, framing systems and roof structures, including trusses and shielding materials, have been developed during recent years. The reasons behind the growing popularity of these products include their ease of fabrication, high strength/weight ratio and suitability for a wide range of applications. These advantages can result in more cost-effective designs, as compared with hot-rolled steel, especially in short-span applications. It has been common practice in cold-formed steel construction to cut openings in the web of beams for the passage of service ducts and piping. The provision of such openings reduces the story heights and consequently can result in saving of considerable amount of construction materials. On the other hand, the presence of a large web opening causes localized redistribution of stress around the opening region. The large opening causes loss of strength and changes the buckling characteristics of an entire member. It also affects the flexural stiffness, resulting in poor performance of member under serviceability. It is common practice to reinforce the opening of hot-rolled steel members, but proper reinforcement schemes for CFS perforated members has not been established yet. Various reinforcement schemes for cold-formed steel sections were investigated during this study. Two types of reinforcement schemes (for flexural zones and shear zones) were developed. Fifty-four flexural tests and 33 shear tests were conducted. Two types of sections (lipped channel joists with h/t ratio 180 and 118) were tested in flexure and one type of section (lipped channel joists with h/t ratio 180) was tested in shear. The study also included a finite element based numerical investigation, consisting of parametric studies on the size (web depth and thickness) of joists, size and shape of web openings, reinforcement and associated fastening schemes. It was observed that a 75 percent of opening in the web of CFS channel joist causes up to 25 percent reduction in flexural strength and up to 60 percent reduction in shear strength. Such reduced flexural and shear strengths were re-captured by providing proper reinforcement schemes. The flexural reinforcement schemes recommended by the current AISI Standard were found to be ineffective for the sections having low w /t ratios. Bridging channel reinforcement scheme was also considered in this study. Bridging channel reinforcement scheme was capable of restoring the flexural strength of cold formed steel joist having w /t ratios 118 and 180. Similarly, the reinforcement schemes recommended in AISI Standard were not adequate to restore the shear strength of joist sections. A newly developed Virendeel type reinforcement system was capable of restoring the original shear strength of a cold-formed steel joist section. / Thesis / Doctor of Philosophy (PhD)