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Formulation of flow number of asphalt mixes using a hybrid computational method

Formulation of flow number of asphalt mixes using a hybrid computational method,10.1016/j.conbuildmat.2010.09.010,Construction and Building Materials,

Formulation of flow number of asphalt mixes using a hybrid computational method   (Citations: 9)
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A high-precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed constitutive model correlates the flow number of Marshall specimens with the percentages of filler, bitumen, voids in mineral aggregate, Marshall stability, and Marshall flow. The comprehensive experimental database used for the development of the model was established upon a series of uniaxial dynamic creep tests conducted in this study. Generalized regression neural network and multiple regression-based analyses were performed to benchmark the GP/SA model. The contributions of the variables affecting the flow number were evaluated through a sensitivity analysis. A subsequent parametric study was carried out and the trends of the results were confirmed with the results of the experimental study. The results indicate that the proposed GP/SA model is effectively capable of evaluating the flow number of asphalt mixtures. The derived model is remarkably straightforward and provides an analysis tool accessible to practicing engineers.
Journal: Construction and Building Materials - CONSTR BUILD MATER , vol. 25, no. 3, pp. 1338-1355, 2011
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    • ...In general, GP is defined as a specialization of genetic algorithms (GA) where the solutions are computer programs rather than binary strings [6, 7]. The main advantage of the GP-based approaches over the conventional modeling and ANN techniques is their ability to generate prediction equations...
    • ...The developed equations can be easily manipulated in practical circumstances [7]...
    • ...SA is very useful for solving several types of optimization problems with nonlinear functions and multiple local optima [7]...
    • ...The GP/SA approach has been rarely applied to civil engineering problems [7, 16]...
    • ...GP solutions are computer programs that are represented as tree structures and expressed in a functional programming language (such as LISP) [5, 7]. In other words, programs (individuals) evolved by GP are parse trees whose length can vary throughout the run [5]...
    • ...GP gives the structure of the approximation model together with the values of its parameters [7, 12]...
    • ...Thus, fitness function is the objective function that GP optimizes [7, 17, 18]...
    • ...The functions and terminals are chosen at random and constructed together to form a computer model in a tree-like structure with a root point with branches extending from each function and ending in a terminal [7]...
    • ...new individuals by reproduction, crossover, and mutation [5, 7]. By the reproduction operation, a part of individuals are copied into the next generation without any change...
    • ...A typical crossover operation is shown in Fig. 2. As can be seen in this figure, two new child computer programs (Child 1, Child 2) are generated from two parental computer programs (Parent 1, Parent 2). The randomly generated crossover points are shown by dotted lines [7]...
    • ...Figure 3 shows a typical mutation operation in GP. The best program that appeared in any generation, the best-so-far solution, defines the output of the GP algorithm [5, 7]...
    • ...During the cooling process, each atom takes a specific position in the metal crystalline structure [7]...
    • ...At each temperature within the annealing process, the atoms are allowed to adjust to a stable equilibrium state of least energy if the temperature does not decrease quickly [7]...
    • ...It is obvious that changing the metal crystalline structure, through the annealing, is associated with a change in the internal energy as DE. However, as the metal temperature gradually drops down, the overall trend of the internal energy change follows a decreasing process, but sometimes the energy may increase by chance [7]...
    • ...The objective function corresponds to the energy state, and moving to any new set of design variables corresponds to a change of the crystalline structural state [7]...
    • ...On the basis of the descriptions given for GP, LGP, and SA, the integrated GP/SA algorithm uses the following main steps to evolve a computer program [7, 15, 19]:...
    • ...Alternatively, if the parent program is not replaced by the child, it remains as the parent program for the next cycle [7]...
    • ...the training process is an important issue for verifying reliability of final models [7, 21]...
    • ...The following multi-objective strategy was considered to choose the best GP and GP/SA models [7]:...
    • ...Various parameters involved in the GP/SA algorithm are shown in Table 3. The parameters were selected based on some previously suggested values [7, 16] and also after a trial and error approach...
    • ...The success of the GP/SA algorithm usually increases with increasing the initial and maximum program size parameters [7]...
    • ...coefficient between experimental and predicted values (Ro 02 ) should be close to 1 [7]...
    • ...Conversely, most conventional methods (like regression and finite element method) need prior knowledge about the nature of the relationships among the data [7]...
    • ...Therefore, it is suggested to use robust algorithms such as GAs for optimally controlling the parameters of runs [7]...

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