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.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

[pt] A construção de árvores de decisão é um problema central em diversas áreas da ciência da computação, por exemplo, teoria de banco de dados e aprendizado computacional. Este problema pode ser visto como o problema de avaliar uma função discreta, onde para verificar o valor de cada variável da função temos que pagar um custo, e os pontos onde a função está definida estão associados a uma distribuição de probabilidade. O objetivo do problema é avaliar a função minimizando o custo gasto (no pior caso ou no caso médio). Nesta tese, apresentamos quatro contribuições relacionadas a esse problema. A primeira é um algoritmo que alcança uma aproximação de O(log(n)) em relação a tanto o custo esperado quanto ao pior custo. A segunda é um método que combina duas árvores, uma com pior custo W e outra com custo esperado E, e produz uma árvore com pior custo de no máximo (1+p)W e custo esperado no máximo (1/(1-e-p))E, onde p é um parâmetro dado. Nós também provamos que esta é uma caracterização justa do melhor trade-off alcançável, mostrando que existe um número infinito de instâncias para as quais não podemos obter uma árvore de decisão com tanto o pior custo menor que (1 + p)OPTW(I) quanto o custo esperado menor que (1/(1 - e - p))OPTE(I), onde OPTW(I) (resp. OPTE(I)) denota o pior custo da árvore de decisão que minimiza o pior custo (resp. custo esperado) para uma instância I do problema. A terceira contribuição é um algoritmo de aproximação de O(log(n)) para a minimização do pior custo para uma variante do problema onde o custo de ler uma variável depende do seu valor. Nossa última contribuição é um algoritmo randomized rounding que, dada uma instância do problema (com um inteiro adicional (k > 0) e um parâmetro 0 < e < 1/2, produz uma árvore de decisão oblivious com custo no máximo (3/(1 - 2e))ln(n)OPT(I) e que produz no máximo (k/e) erros, onde OPT(I) denota o custo da árvore de decisão oblivious com o menor custo entre todas as árvores oblivious para a instância I que produzem no máximo k erros de classificação. / [en] Decision tree construction is a central problem in several areas of computer science, for example, data base theory and computational learning. This problem can be viewed as the problem of evaluating a discrete function, where to check the value of each variable of the function we have to pay a cost, and the points where the function is defined are associated with a probability distribution. The goal of the problem is to evaluate the function minimizing the cost spent (in the worst case or in expectation). In this Thesis, we present four contributions related to this problem. The first one is an algorithm that achieves an O(log(n)) approximation with respect to both the expected and the worst costs. The second one is a procedure that combines two trees, one with worst costW and another with expected cost E, and produces a tree with worst cost at most (1+p)W and expected cost at most (1/(1-e-p))E, where p is a given parameter. We also prove that this is a sharp characterization of the best possible trade-off attainable, showing that there are infinitely many instances for which we cannot obtain a decision tree with both worst cost smaller than (1+p)OPTW(I) and expected cost smaller than (1/(1-e-p))OPTE(I), where OPTW(I) (resp. OPTE(I)) denotes the cost of the decision tree that minimizes the worst cost (resp. expected cost) for an instance I of the problem. The third contribution is an O(log(n)) approximation algorithm for the minimization of the worst cost for a variant of the problem where the cost of reading a variable depends on its value. Our final contribution is a randomized rounding algorithm that, given an instance of the problem (with an additional integer k > 0) and a parameter 0 < e < 1/2, builds an oblivious decision tree with cost at most (3/(1 - 2e))ln(n)OPT(I) and produces at most (k/e) errors, where OPT(I) denotes the cost of the oblivious decision tree with minimum cost among all oblivious decision trees for instance I that make at most k classification errors.

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)