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Long-term prediction of the performance and durability of pavement represents a critical and vital issue in the pavement surface type selection process by the Kansas Department of Transportation (KDOT) using the life-cycle-cost analysis. Accurate prediction of roughness progression on Portland Cement Concrete (PCC) pavements is very important since the current model used by KDOT is based on the pavement serviceability guidelines (1993 AASHTO Design Guide). In this study, dynamic Artificial Neural Network (ANN) and statistical analysis approaches were used to develop reliable and accurate time-dependent roughness (International Roughness Index, IRI) prediction models for the newly constructed Kansas Jointed Plain Concrete Pavements (JPCP). To achieve this objective, data used in the model development process include construction and materials data as well as other inventory items, such as, traffic and climatic related data, which reflect the section-specific local conditions in Kansas.
Longitudinal joints of concrete pavements constitute essential elements in the overall functionality and performance of the pavement system. The noticeable deterioration of these joints on some of Kansas highways lead to performing a preliminary study in order to determine the severity of the problem and analyze the design and construction issues that might have contributed to the observed damage. Data was collected from 31 sections of interstate highway in the Kansas City, Kansas metropolitan area. Parameters such as the age of the pavement and sealant, accumulated volumes of total and heavy traffic, type of joint, slab length, slab thickness, concrete strength, steel reinforcement, etc. were analyzed in relation to the observed spalling of the longitudinal joints. The variation of these parameters, with the spalling measures presented in this report, was studied.
Premature deterioration of concrete pavement due to D-Cracking has been a problem in Kansas since the 1930s. Limestone is the major source of coarse aggregate in eastern Kansas where the majority of the concrete pavements are constructed. D-Cracking field performance was investigated to determine whether aggregate freeze-thaw durability specifications implemented in the 1980s have reduced materials-related failures to an acceptable level. The results indicate that the failure rate has decreased, but not to an acceptable level. Limestone source material appears to be the dominant parameter affecting D-Cracking, while other design parameters, such as base type, joint sealant type, joint spacing, and joint orientation do not appear to significantly affect the presence of D-Cracking. Subsidiary aggregate-related reaction mechanisms were observed at locations with surface D-Cracking likely due to the increased amount of water penetration. The subsidiary reactions generally do not appear to be present at locations away from the D-Cracking. Kansas Department of Transportation policies (any actions, requirements, or decisions) that affect the risk of D-Cracking were reviewed. Recommendations included implementation of field performance criteria for aggregate material sources, improvement and unification of quarry monitoring and sampling procedures, to perform life-cycle cost analyses for various aggregate materials in concrete, and future monitoring of quarry field performance.
The newest volume in this series presents refereed papers in the following categories and their applications in the engineering domain: Neural Networks; Complex Networks; Evolutionary Programming; Data Mining; Fuzzy Logic; Adaptive Control; Pattern Recognition; Smart Engineering System Design. These papers are intended to provide a forum for researchers in the field to exchange ideas on smart engineering system design.