Prediction of rutting potential of dense bituminous mixtures with polypropylene fibers via repeated creep testing by using neuro-fuzzy approach

This study investigates the potential use of the neuro-fuzzy (NF) approach to model the rutting prediction by the aid of repeated creep testing results for polypropylene modified asphalt mixtures. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content have been tested in order to predict their rutting potential under different load values and loading patterns at 50°C. Throughout the testing phase, it has been clearly shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing an eminent increase of the service life of samples under repeated creep testing. The performance of the accuracy of proposed neuro-fuzzy model is observed to be quite satisfactory. In addition, to obtain the main effects plot, a wide range of detailed two and three dimensional parametric studies have been performed

Modelling Marshall Design Test Results of Polypropylene Modified Asphalt by Genetic Programming Techniques

Determining Marshall design test results is time consuming. If the researchers can obtain stability and flow values by mechanical testing, rest of the calculations will just be mathematical manipulations. Marshall stability and flow tests were carried out on specimens fabricated with dierent type of polypropylene fibers. It has been shown that addition of polypropylene fibers improved Marshall stabilities and Marshall quotient values in a considerable manner. Input variables in the developed genetic programming model use the physical properties of standard Marshall specimens such as polypropylene type, polypropylene percentage, bitumen percentage, specimen height, calculated unit weight, voids in mineral aggregate, voids filled with asphalt and air voids. Performance of the genetic programming model is quite satisfactory. Besides, to obtain main eects plot, a wide range of parametric studies have been performed.The presented closed form solution will also help further researchers willing to perform similar studies, without carrying out destructive tests

Determination of the Optimal Polypropylene Fiber Addition to the Dense Bituminous Mixtures by the Aid of Mechanical and Optical Means

Polypropylene fibers are completely native modifiers and they do not have any dependence on abroad in case of technology. When viewed from this point, the modification of bituminous binders with polypropylene fibers is a very important step for our country’s “economical” concerns. In this study, first of all, the physical and chemical effects of polypropylene fibers on bitumen were investigated. Next, the amount of “optimum” polypropylene fibers that has to be added into the mixture was determined. In order to determine it, first, static creep tests and Marshall tests were carried out and then, images of the polypropylene fiber added bituminous binders under fluorescence microscopy were researched. With the application of physical and mechanical tests to the Marshall specimens prepared with the optimum polypropylene amount that was obtained, optimum bitumen content was determined and finally economical analyses were carried out. By carrying out extensive analyses it was seen that the utilisation of polypropylene fibers improves the physical and mechanical properties of the resultant asphalt mixture mainly by enhancing the permanent deformation resistance. On the other hand, polypropylene modification results in 30% economy from bitumen which is a clear indication of the benefit in the mass production of asphalt concrete

A new approach to neural trip distribution models: NETDIM

This paper develops and presents a new neural network approach to model trip distribution, which is one of the important phases of conventional four-step travel demand modelling. The trip distribution problem has been investigated using back-propagation artificial neural networks in a number of studies and it was concluded that back-propagation artificial neural networks underperform when compared to traditional models. Such underperformance is due to the thresholding of the linearly combined inputs by utilising a non-linear function and carrying out this operation both in hidden and output layers. The proposed neural trip distribution model does not threshold the linearly combined outputs from the hidden layer. This makes it different from back-propagation artificial neural networks where combined inputs from the hidden layer are activated once more in the output layer. In addition, the neuron in the output layer is used as a summation unit in contrast to the methodologies cited in the neural network applications literature. At the same time, the bias neuron is not connected to the output neuron in the output layer. When this model is compared with various approaches such as the gravity model, modular neural networks and back-propagation neural networks, it was concluded that this new model provides better prediction of trip distribution and therefore, outperforms all the existing approaches

Rutting prediction of asphalt mixtures modified by polypropylene fibers via repeated creep testing by utilising genetic programming

A novel application of genetic programming (GP) for modelling and presenting closed form solutions to the rutting prediction for polypropylene (PP) modified asphalt mixtures is investigated. Various PP fibers have been utilised for bitumen modification and repeated creep (RC) tests have been carried out. Marshall specimens, fabricated with multifilament 3 mm (M-03) type PP fibers at optimum bitumen content of 5% have been tested under different load values and patterns at 50 °C to investigate their rutting potential. It has been shown that the service lives of PP fiber-reinforced Marshall specimens are respectively longer than the control specimens under the same testing conditions (5 to 12 times). Input variables in the developed GP model use the physical properties of Marshall specimens such as PP type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids, rest period and pulse counts. The performance of the accuracy of the proposed GP model is observed to be quite satisfactory. To obtain the main effects plot, detailed parametric studies have been performed. The presened closed form solution will also help further researchers willing to perform studies on the prediction of the rutting potential of asphalt without carrying out destructive tests for similar type of aggregate sources, bitumen, aggregate gradation, modification technique and laboratory conditions

Estimation of Polypropylene Concentration of Modified Bitumen Images by Using k-NN and SVM Classifiers

The goal of this study is to design an expert system that automatically classifies the microscopic images of polypropylene fiber (PPF) modified bitumen including seven different contents of fibers. Optical microscopy was used to capture the images from thin films of polypropylene fiber modified bitumen samples at a magnification scale of 100 x. A total of 313 images were pre-processed, and features were extracted and selected by the exhaustive search method. The k-nearest neighbor (k-NN) and multiclass support vector machine (SVM) classifiers were applied to quantify the representation capacity. The k-NN and multiclass SVM classifiers reached an accuracy rate of 87% and 86%, respectively. The results suggest that the proposed expert system can successfully estimate the concentration of PPF in bitumen images with good generalization characteristics. (C) 2014 American Society of Civil Engineers