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Accumulated strain prediction of polypropylene modified marshall specimens in repeated creep test using artificial neural networks

This study presents an application of artificial neural networks (ANN) for the prediction of repeated creep test results for polypropylene (PP) modified asphalt mixtures. Polypropylene fibers are used to modify the bituminous binder in order to improve the physical and mechanical properties of the resulting asphaltic mixture. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content were tested using universal testing machine (UTM-5P) in order to determine their rheological/creep behavior under repeated loading. Different load values and loading patterns have been applied to the previously prepared specimens at a predetermined temperature. It has been shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing the increase of the service life of samples under repeated creep testing. The proposed ANN model uses the physical properties of standard Marshall specimens such as polypropylene type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids and repeated creep test properties such as rest period and pulse counts in order to predict the accumulated strain values obtained at the end of mechanical tests. Moreover parametric analyses have been carried out. The results of parametric analyses were used to evaluate the accumulated strain of the Marshall specimens subjected to repeated load creep tests in a quite well manner.

Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks

This study presents an application of neural networks (NN) for the prediction of Marshall test results for polypropylene (PP) modified asphalt mixtures. PP fibers are used to modify the bituminous binder in order to improve the physical and mechanical properties of the resulting asphaltic mixture. Marshall stability and flow tests were carried out on specimens fabricated with different type of PP fibers and also waste PP at optimum bitumen content. It has been shown that the addition of polypropylene fibers results in the improved Marshall stabilities and Marshall Quotient values, which is a kind of pseudo stiffness. The proposed NN model uses the physical properties of standard Marshall specimens such as PP type, PP percentage, bitumen percentage, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt and air voids in order to predict the Marshall stability, flow and Marshall Quotient values obtained at the end of mechanical tests. The explicit formulation of stability, flow and Marshall Quotient based on the proposed NN model is also obtained and presented for further use by researchers. Moreover parametric analyses have been carried out. The results of parametric analyses were used to evaluate mechanical properties of the Marshall specimens in a quite well manner.

Rutting analysis of 100 mm diameter polypropylene modified asphalt specimens using gyratory and Marshall compactors

Compaction technique used in Marshall design does not model the process of actual rolling procedures on site exactly. Carrying out laboratory compaction of dense bituminous mixtures with Superpave gyratory compactors is a more realistic way of simulating actual compaction. In this study, mechanical differences of reference and polypropylene modified asphalt mixtures were compared using Superpave gyratory and Marshall compaction methods by carrying out repeated creep tests utilising universal testing machine. In addition, there is no standard Superpave design procedure for 100 mm diameter samples till date. The other purpose of this study is to propose new standards for the compaction and testing procedures of these 100 mm specimens. Indeed, extensive studies have shown that the design gyration number should be 40 for reference and 33 for polypropylene modified specimens under medium traffic conditions for the similar and specific type of aggregate sources, bitumen, aggregate gradation, mix proportioning, modification technique and laboratory conditions. Moreover, it was shown that, the asphalt samples produced by Superpave gyratory compactor were much resistant to destructive rutting effects than the asphalt specimens prepared by Marshall design.

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 presented 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.

Polypropylene fiber-reinforced bitumen

Abstract: This chapter discusses the physical and mechanical behaviour of polypropylene fiber-reinforced modified asphalt mixtures. The chapter first reviews a general introduction to polypropylene modification of asphalt concrete. Then dry basis modification with polypropylene fibers and fatigue life improvement of asphalt concrete are discussed. Wet basis modification of asphalt with polypropylene fibers and repeated creep behaviour of bituminous concrete are introduced next. The utilisation of artificial neural networks for the prediction of Marshall test results of polypropylene modified dense bituminous mixtures is also investigated. Then a novel approach utilising closed-form solutions and parametric studies to predict the strain accumulation of polypropylene-modified Marshall specimens in repeated creep tests is proposed. Determination of optimal polypropylene fiber modification of asphalt concrete and the relevant mechanical and optical tests to fulfil this aim are also utilised. Finally, conclusions and a short commentary on likely future trends are introduced.

A new approach to neural trip distribution models: NETDIM

Abstract 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.

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

Estimation of fatigue lives of fly ash modified dense bituminous mixtures based on artificial neural networks

This study deals with estimation of fatigue lives of bituminous mixtures using artificial neural networks. Different types of fly ash were used as filler replacing agents in a dense bituminous mixture. Fatigue tests were performed using repeated load indirect tensile test apparatus under controlled stress conditions. For determination of fatigue life, the initiation of macro crack was accepted as the main criteria to terminate the test. The full-scale tests on asphalt pavement sections are very expensive and these tests require many years in order to be completed and sometimes do not end up with solid conclusions. Therefore, the determination of fatigue lives of bituminous mixtures in the laboratory environment is very important. This study used the experimental data as training set and, with proposed neural network architecture, very reasonable estimates of fatigue lives of bituminous mixtures have been obtained. The proposed approach provides real economy, time saving and allows observing the effect of fly ash replacement and composition on the mechanical properties of mixtures such as fatigue lives and their estimations without carrying out destructive tests.