Hot Mix Asphalt (HMA) overlay is a major rehabilitation treatment for the existing deteriorated pavements (both flexible and rigid pavements). Reflective cracking (RC) is the most common distress type appearing in the HMA overlays which structurally and functionally degrades the whole pavement structure, especially under high traffic volume. Although many studies have been conducted to identify the best performing RC mitigation technique, the level of success varies from premature failure to good performance in the field. In Florida, Asphalt Rubber Membrane Interlayers (ARMIs) have been used as a RC mitigation technique but its field performance has not been successful. In this study, the best performing means to mitigate RC in the overlays considering Florida’s special conditions have been investigated. The research methodology includes (1) extensive literature reviews regarding the RC mechanism and introduced mitigation options, (2) nationwide survey for understanding the current practice of RC management in the U.S., and (3) the development of decision trees for the selection of the best performing RC mitigation method. Extensive literature reviews have been conducted to identify current available RC mitigation techniques and the advantages and disadvantages of each technique were compared. Lesson learned from the collected case studies were used as input for the selection of the best performing RC mitigation techniques for Florida’s roads. The key input parameters in selecting optimum mitigation techniques are: 1) overlay characterization, 2) existing pavement condition, 3) base and subgrade structural condition, 4) environmental condition and 5) traffic level. In addition, to understand the current iv practices how reflective cracking is managed in each state, a nationwide survey was conducted by distributing the survey questionnaire (with the emphasis on flexible pavement) to all other highway agencies. Based on the responses, the most successful method of treatment is to increase the thickness of HMA overlay. Crack arresting layer is considered to be in the second place among its users. Lack of cost analysis and low rate of successful practices raise the necessity of conducting more research on this subject. Considering Florida’s special conditions (climate, materials, distress type, and geological conditions) and the RC mechanism, two RC mitigation techniques have been proposed: 1) overlay reinforcement (i.e. geosynthetic reinforcement) for the existing flexible pavements and 2) Stress Absorbing Membrane Interlayer (SAMI) for the existing rigid pavements. As the final products of this study, decision trees to select an optimum RC mitigation technique for both flexible and rigid pavements were developed. The decision trees can provide a detailed guideline to pavement engineer how to consider the affecting parameters in the selection of RC mitigation technique.

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A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in traditional decision tree induction algorithms, when applied in the pavement treatment strategies. The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial attribute data in the pavement database. With the proposed algorithm, one distinct event occurrence can associate with two or multiple attribute values that occur simultaneously in spatial and temporal domains. This research dissertation describes the details of the proposed CL-DT algorithms and steps of realizing the proposed algorithm. First, the dissertation research describes the detailed colocation mining algorithm, including spatial attribute data selection in pavement databases, the determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a neighborhood relationship-based spatial joins of table instances for many prevalent co-locations with one prevalent co-location; and the distance event-type constraint condition is a Euclidean distance between a set of attributes and its corresponding clusters center of attributes. The dissertation research also developed the spatial feature pruning method using the multi-resolution pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection in the pavement management database, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. In this step, co-location mining rules are used to guide the decision tree generation and induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one's expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes. / Ph. D.

Yes / The present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine-salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes. / Commonwealth Scholarship Commission in the UK (ZMCS-2018-783) and Engineering and Physical Sciences Research Council (EPSRC EP/J003360/1 and EP/L027011/1)