155 research outputs found

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

    Get PDF
    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

    Number of design gyrations for 100 mm compacted asphalt mixtures modified with polypropylene

    Get PDF
    There is no standard accepted by highway authorities on the compaction procedures of 100 mm diameter gyratory compactor specimens. In previous studies on gyratory compaction, the utilisation of either coring from 150 mm specimens or preparing taller specimens than the usual 63.5 mm long Marshall specimens has been undertaken. The utilisation of 150 mm mould sizes introduces an important amount of mechanical disturbance during the coring process of 100 mm diameter specimens out of them. The novelty of the present study is that a new standard for preparing gyratory compactor specimens with a diameter of 100 mm and length of approximately 63.5 mm by utilising polypropylene fibers of 3 mm multifilament for the modification has been proposed for the first time. To achieve optimal conditions, in addition to gyratory compaction, modified bitumen samples’ physical properties, especially rotational viscosities, were also investigated in a detailed manner. Physical and mechanical tests have been carried out using the specimens prepared by changing the main testing parameter of gyration number from 30 to 70. The design gyration number of 100 mm polypropylene fiber reinforced dense bituminous mixtures has been determined as 33 under medium traffic conditions.No sponso

    Providing Laboratory Rutting Models for Modified Asphalt Mixes with Different Waste Materials

    Get PDF
    Due to the complex behavior of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the permanent deformation of asphalt pavement is difficult. This study discusses the application of artificial neural network (ANN) and the multiple linear regression (MLR) in predicting permanent deformation of asphalt concrete mixtures modified by waste materials (waste plastic bottles and waste high-density polyethylene). The use of waste materials in the pavement industry can prevent the accumulation of waste material and environmental pollution and can reduce primary production costs. The results of a laboratory study evaluating the rutting properties of Hot-Mix Asphalt (HMA) mixtures using dynamic creep tests were investigated. The results indicate ANN techniques are more effective in predicting the rutting of the modified mixtures tested in this study than the traditional statistical-based prediction models. On the other hand, results show that an increase in percentage of waste materials is very effective in reducing the final strain of asphalt mixtures. However, an increase in percentage of additives over 7% does not help to reduce permanent deformation under dynamic loading in the asphalt mixtures

    Influence of Polypropylene Length on Stability and Flow of Fiber-reinforced Asphalt Mixtures

    Get PDF
    Engineers are constantly trying to improve the performance of the flexible pavements. The main surface distress types which cause maintenance and disruption are rutting and fatigue cracking. For solving these problems, many studies have been carried out until now, ranged from changing gradation to adding polymers and fibers to asphalt mixture. In this study, polypropylene additive was selected as fiber additive because of low costing and having good correlation with asphalt pavement. Three type of polypropylene additive in the length 6, 12 and 19 mm were selected and used at five different percentages in the asphalt concrete mixture. Asphalt specimens were analysed by Marshall Analysis and finally tested by Marshall Stability apparatus. Adding polypropylene increased Marshall Stability (38%), and decreased Flow (39%). These results show that polypropylene can be helpful for increasing pavement life

    Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network

    Get PDF
    Abstract: Prediction of crack growth under creep condition is prime requirement in order to avoid costly and timeconsuming creep crack growth tests. To predict, in a reliable way, the growth of a major crack in a structural components operating at high temperatures, requires a fracture mechanics based approach. In this Study a novel technique, which uses Finite Element Method (FEM) together with Artificial Neural Networks (ANN) has been developed to predict the fracture mechanics parameter (C*) in a 1%Cr1%MoV low alloy rotor steel under wide range of loading and temperatures. After confirming the validity of the FEM model with experimental data, a collection of numerical and experimental data has been used for training the various neural networks models. Three networks have been used to simulate the process, the perceptron multilayer network with tangent transfer function that uses 9 neurons in the hidden layer, gives the best results. Finally, for validation three case studies at 538°C, 550°C and 594°C temperatures are employed. The proposed model has proved that a combinations of ANN and FEM simulation performs well in estimation of C* and it is a powerful designing tool for creep crack growth characterization

    analysis of the mechanical behaviour of asphalt concretes using artificial neural networks

    Get PDF
    The current paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using artificial neural networks (ANNs). The asphalt concrete mixes considered in this study have been prepared with a diabase aggregate skeleton and two different types of bitumen, namely, a conventional bituminous binder and a polymer-modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall stability, flow, quotient, and moreover by the stiffness modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feedforward type, were characterized by one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with coefficients of correlation in the testing phase within the range 0.98798–0.91024, depending on the considered model, thus demonstrating the feasibility to apply ANN modelization to predict the mechanical and performance response of the asphalt concretes investigated. Furthermore, a closed-form equation has been provided for each of the four ANN models developed, assuming as input parameters the production process, the bitumen type and content, the filler/bitumen ratio, and the volumetric properties of the mixes. Such equations allow any other researcher to predict the mechanical parameter of interest, within the framework of the present study

    Prediction of the High-Temperature Performance of a Geopolymer Modified Asphalt Binder using Artificial Neural Networks

    Get PDF
    Complexity in the behaviour of an asphalt binder is further escalated with geopolymer (fly ash and alkali liquid) modification, thus making it difficult to accurately predict the performance of the binder. This study employs artificial neural network modelling to predict the complex shear modulus, storage modulus, loss modulus and phase angle outcomes of experimental results from dynamic shear rheometer (DSR) oscillation tests under four separate scenarios. The proposed artificial neural network models received test conditions (temperature and frequency) and three different geopolymer concentrations (3%, 5% and 7% by the weight of bitumen) as the predictor parameters.  The variants of the optimal algorithms were Levenberg-Marquardt (LM), Scaled conjugate gradient and Polak-Ribiere conjugate gradient (CPG) training algorithms with different combinations of network structures and tan-sig and log-sig as activation functions. The coefficient of determination, covariance and root mean square error (RMSE) were used as statistical measures of model prediction performance. Based on the statistical performance indicators, the LM algorithm with a 3-5-1 network architecture and tan-sig as the activation function was the best performing model for predicting the complex modulus with R2 values of 0.996 for the training dataset and 0.971 for the testing dataset and RMSE values of 0.118 and 0.139 for the training and testing datasets, respectively. Furthermore, it was observed that the least efficient model was the phase angle prediction model developed with the CPG training algorithm, which had a 3-8-1 network architecture and log-sig as the activation function.  The model yielded R2 values of 0.909 and 0.829 for the training and testing datasets, respectively. Poor prediction performance for the testing dataset indicated that the model was unable to learn complexity in the data and would perform below a significance level of 0.90 in predicting using untrained data

    Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

    Get PDF
    In this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers
    • …
    corecore