Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Connecting: the use of information and communication technologies by older adults in a retirement community

This is a long-term study of the use of information and communication technologies by 30 older adults (ages 70–97) living in a large retirement community. The study spanned the years of 1996 to 2008, during which time the research participants grappled with the challenges of computer use while aging 12 years. The researcher, herself a ‘mature learner,’ used a qualitative research design which included observations and open-ended interviews. Using a strategy of “intermittent immersion,” she spent an average of two weeks per visit on site and participated in the lives of the research population in numerous ways, including service as their computer tutor. With e-mail and telephone contact, she was able to continue her interactions with participants throughout the 12-year period. A long-term perspective afforded the view of the evolution, devolution or cessation of the technology use by these older adults, and this process is chronicled in detail through five individual “profiles.” Three research questions dominated the inquiry: What function do computers serve in the lives of older adults? Does computer use foster or interfere with social ties? Is social support necessary for success in the face of challenging learning tasks? In answer to the first question, it became clear that computers were valued as a symbol of competence and intelligence. Some individuals brought their computers with them when transferred to the single-room residences of assisted living or nursing care facilities. Even when use had ceased, their computers were displayed to signal that their owners were or had once been keeping up to date. In answer to the second question, computer owners socialized around computing use (with in-person family members or friends) more than, or as much as, they socialized through their computers in the digital realm of the Internet. And in answer to the third question, while the existence of social support did facilitate computer exploration, more important was the social support network generated and developed among fellow computer users.