Design of unique composites based on aromatic thermosetting copolyesters

Aromatic thermosetting copolyester (ATSP) has promise in high-temperature applications. It can be employed as a bulk polymer, as a coating and as a matrix for carbon fiber composites (ATSP/C composites). This work focuses on the applications of high performance ATSP/C composites. The morphology of the ATSP matrix in the presence of carbon fiber was studied. The effect of liquid crystalline character of starting oligomers used to prepare ATSP on the final crystal structure of the ATSP/C composite was evaluated. Matrices obtained by crosslinking of both liquid crystalline oligomers (ATSP2) and non-liquid crystalline oligomers (ATSP1) tend to crystallize in presence of carbon fibers. The crystallite size of ATSP2 is 4 times that of ATSP1. Composites made from ATSP2 yield tougher matrices compared to those made from ATSP1. Thus toughened matrices could be achieved without incorporating any additives by just changing the morphology of the final polymer. The flammability characteristics of ATSP were also studied. The limiting oxygen index (LOI) of bulk ATSP was found to be 40% whereas that of ATSP/C composites is estimated to be 85%. Thus, ATSP shows potential to be used as a flame resistant material, and also as an aerospace reentry shield. Mechanical properties of the ATSP/C composite were characterized. ATSP was observed to bond strongly with reinforcing carbon fibers. The tensile strength, modulus and shear modulus were comparable to those of conventionally used high temperature epoxy resins. ATSP shows a unique capability for healing of interlaminar cracks on application of heat and pressure, via the Interchain Transesterification Reaction (ITR). ITR can also be used for reduction in void volume and healing of microcracks. Thus, ATSP resin systems provide a unique intrinsic repair mechanism compared to any other thermosetting systems in use today. Preliminary studies on measurement of residual stresses for ATSP/C composites indicate that the stresses induced are much lower than that in epoxy/C composites. Thermal fatigue testing suggests that ATSP shows better resistance to microcracking compared to epoxy resins.

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.