Prediction of permanent deformation in asphalt mixtures

An asphalt mixture is combined of different sizes of aggregate, filler, and bitumen for application on the most common road construction materials. In asphalt pavement material there are different types of distress such as permanent deformation (rutting), fatigue cracking, ravelling, potholes, stripping, etc. There are many reasons for these types of distress, some of them related to the pavement structure, e.g. whether the underlying layers are weak, others related to the mixture properties. Other causes could be related to external conditions such as high temperature, high axle load, long duration of load application, etc. This research has focused on the permanent deformation (rutting) as a function of aggregate gradation. The aggregate gradations of more than twenty asphalt mixtures, manufactured with different gradations, were analysed by using the Bailey method of gradation analysis. The analysis was performed in relation to Repeated Load Axial Test (RLAT) testing results to study the performance of each mixture. The results showed that the Bailey method is not capable on its own to define the differences between the gradations of each mixture. Therefore, three more packing ratios were introduced to adequately describe the aggregate gradation. The aggregate particle packing was extensively studied through these packing ratios and it was shown how the different particle sizes interact with each other. Images were taken for two mixtures to validate the theory behind the ratios. The five packing ratios (two of Bailey and three new ratios) were used in Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques for all the mixtures as input data to predict the mixture performance (RLAT permanent deformation and Indirect Tensile Stiffness Modulus ITSM stiffness modulus) and they showed good prediction capability. After establishing the impact of aggregate packing on the performance, six mixtures were re-manufactured and re-tested with different variables; the selection of the mixtures was made to cover a range of different gradations (ratios). The aim of this step was to understand the effect of these variables on the asphalt mixture in the light of the packing ratios. The variables that were used were binder content, testing temperature and compaction effort. The binder content results showed an interesting effect on the permanent deformation and stiffness of the asphalt mixture. The packing of aggregate was very helpful in understanding the different mixture behaviour with different binder content. The effect of aggregate packing was not shown at relatively low testing temperature, but as the temperature rises the aggregate packing effect starts to appear. The effect of compaction which was represented by the number of gyrations in gyratory compactor was inconsistent; results show over-compaction can lead to poor performance. Finally, a linear viscous method was introduced aiming to predict the rutting in an asphalt mixture. The method was based on using a multilayer linear programme (BISAR) and using viscous parameters of the mixture as input. The non-linear properties of the material were incorporated by using the RLAT test. For this purpose, six mixtures were used and tested in a wheel tracking machine. The predicted results were compared with the wheel tracking rut depth in the laboratory and showed good agreement at different temperatures. However, at high temperature (50 °C) the material properties in the RLAT test did not behave as linear viscous, which resulted in a much poorer prediction. Trials were made to predict field rut but it was found that special requirements were needed for the approach which were not available at the time of the research. However, for the available field data, the method was found to be a good predictor

Use of data mining techniques to explain the primary factors influencing water sensitivity of asphalt mixtures

The water sensitivity of asphalt mixtures affects the durability of the pavements, and it depends on several parameters related to its composition (aggregates and binder) and the production and application processes. One of the main parameters used in the European Standards to measure the water sensitivity of asphalt mixtures is the indirect tensile strength ratio (ITSR). Therefore, this work aims to obtain a predictive model of ITSR of asphalt mixtures using several parameters that affect water sensitivity and assess their relative importance. The database used to develop the model comprises thirteen parameters collected from one hundred sixty different asphalt mixtures. Data Mining techniques were applied to process the data using Multiple Regression, Artificial Neural Networks, and Support Vector Machines (SVM). The different metrics analysed showed that SVM is the best predictive model of the ITSR (mean absolute deviation of 0.116, root mean square error of 0.150 and Pearson correlation coefficient of 0.667). The application of a sensitivity analysis indicates that the binder content is the parameter that most influences the water sensitivity of asphalt mixtures (26%). However, this property depends simultaneously on other factors such as the characteristics of the coarse and fine aggregates (24.9%), asphalt binder characteristics (19.3%) and the use of additives (10%).Acknowledgements This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R & D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE) , under reference UIDB/04029/2020

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

Prediction of permanent deformation in asphalt mixtures

An asphalt mixture is combined of different sizes of aggregate, filler, and bitumen for application on the most common road construction materials. In asphalt pavement material there are different types of distress such as permanent deformation (rutting), fatigue cracking, ravelling, potholes, stripping, etc. There are many reasons for these types of distress, some of them related to the pavement structure, e.g. whether the underlying layers are weak, others related to the mixture properties. Other causes could be related to external conditions such as high temperature, high axle load, long duration of load application, etc. This research has focused on the permanent deformation (rutting) as a function of aggregate gradation. The aggregate gradations of more than twenty asphalt mixtures, manufactured with different gradations, were analysed by using the Bailey method of gradation analysis. The analysis was performed in relation to Repeated Load Axial Test (RLAT) testing results to study the performance of each mixture. The results showed that the Bailey method is not capable on its own to define the differences between the gradations of each mixture. Therefore, three more packing ratios were introduced to adequately describe the aggregate gradation. The aggregate particle packing was extensively studied through these packing ratios and it was shown how the different particle sizes interact with each other. Images were taken for two mixtures to validate the theory behind the ratios. The five packing ratios (two of Bailey and three new ratios) were used in Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques for all the mixtures as input data to predict the mixture performance (RLAT permanent deformation and Indirect Tensile Stiffness Modulus ITSM stiffness modulus) and they showed good prediction capability. After establishing the impact of aggregate packing on the performance, six mixtures were re-manufactured and re-tested with different variables; the selection of the mixtures was made to cover a range of different gradations (ratios). The aim of this step was to understand the effect of these variables on the asphalt mixture in the light of the packing ratios. The variables that were used were binder content, testing temperature and compaction effort. The binder content results showed an interesting effect on the permanent deformation and stiffness of the asphalt mixture. The packing of aggregate was very helpful in understanding the different mixture behaviour with different binder content. The effect of aggregate packing was not shown at relatively low testing temperature, but as the temperature rises the aggregate packing effect starts to appear. The effect of compaction which was represented by the number of gyrations in gyratory compactor was inconsistent; results show over-compaction can lead to poor performance. Finally, a linear viscous method was introduced aiming to predict the rutting in an asphalt mixture. The method was based on using a multilayer linear programme (BISAR) and using viscous parameters of the mixture as input. The non-linear properties of the material were incorporated by using the RLAT test. For this purpose, six mixtures were used and tested in a wheel tracking machine. The predicted results were compared with the wheel tracking rut depth in the laboratory and showed good agreement at different temperatures. However, at high temperature (50 °C) the material properties in the RLAT test did not behave as linear viscous, which resulted in a much poorer prediction. Trials were made to predict field rut but it was found that special requirements were needed for the approach which were not available at the time of the research. However, for the available field data, the method was found to be a good predictor

A Neuro-Fuzzy and Neural Network Approach for Rutting Potential Prediction of Asphalt Mixture Based on Creep Test

This study implements the soft computing techniques such as Artificial Neural Network (ANN) and an adaptive Neuro-Fuzzy (ANFIS) approach. Thus to model the rutting prediction with the aid of experimental uniaxial creep test results for asphalt mixtures. Marshall samples, having Maximum Nominal Size of 12.5 mm, have been selected from previous studies. These samples have been prepared and tested under different conditions. They were also subjected to different loading stress (0.034, 0.069, 0.103) MPa, and tested at various temperature (10, 20, 40, and 55) °C. The modeling analysis revealed that both approaches are powerful tools for modeling creep behavior of pavement mixture in terms of Root Mean Square Error and Correlation Coefficient. The best results are obtained with the ANFIS model

Short-term aging performance and simulation of modified binders using adaptive neuro-fuzzy inference system

The influence of polymer/nanocomposites (Acrylete-Styrene-Acrylonitrile (ASA)/ Nanosilica (Si)) asphalt binder aging and performance characteristics was investigated. ASA was used at 5% while nanosilcia was blended in 3, 5 and 7% concentrations by the weight of asphalt. Temperature sensitivity, aging resistance and viscoelastic properties of the asphalt binders were evaluated by conducting physical and dynamic shear rheometer (DSR) testing procedures. The tests were performed under unaged and short-term aged conditions by simulating the aging of asphalt in a Rolling thin film oven (RTFO). Additionally, the Adaptive Neuro-Fuzzy Inference System (ANFIS) modelling technique was adopted to predict the short-term aged behaviour of asphalt binders by using the viscoelastic properties of asphalt in an unaged state. The experimental outcomes from the DSR tests showed that the complex modulus (G*) was increased and the phase angle (δ) was reduced for the modified binders, indicating an improvement in the viscoelastic properties compared to the control asphalt binder. Furthermore, the considerably small difference in the G* and δ between the binders in unaged and RTFO aged states indicated that the modifiers had a positive effect in terms of improving the aging resistance of the asphalt binders. Moreover, the ANFIS model prediction capacity, which was assessed by the Coefficient of Determination (R2) and Mean Squared Error (MSE) and Mean Average Percentage Error (MAPE) was shown to be capable of accurately predicting the short term-aging behaviour of asphalt binders from the asphalt binder viscoelastic properties in an unaged state with an R2 value of 0.977, MSE of 0.00032 and MAPE of 0.286

Numerical and Experimental Investigation on the Modified of Hot Mix Asphalt Concrete Containing Crumb Rubber and Waste Pet

The main objective of this research is to investigate the rutting of modified mixtures with two additives of crumb rubber and polyethylene terephthalate (PET).Therefore, ITS method, resilient modulus and Dynamic creep were used to investigate asphalt behaviour with these two additives in this article. Modified blends include ten blends that are made by combining crumb rubber and polyethylene terephthalate in various percentages. The modifiers are combined in two percent 10% and 15% with 60/70 penetration bitumen. Finally, with these mentioned tests, the results of the ten modified samples along with the non-modified one were compared. The results showed that the addition of polyethylene terephthalate increased the viscosity and reduced the density; therefore, addition of more polyethylene terephthalate in the modifier reduces the flow number and, on the other hand, addition of 15% modifier containing polyethylene terephthalate resilient module increases the flow number by about 66%. Ultimately, neural network method was used to predict the result of dynamic creep test; indirect tensile strength and the capability of neural network method have been measured to estimate the laboratory result. According to the results, ANFIS can estimate the laboratory data correctly

Experimental and numerical investigation of the properties of the Hot Mix Asphalt Concrete with basalt and glass fiber

Abstract. In the recent decades, different kinds of fiber materials are used for improving the asphalt mixture performance. Meanwhile, different kinds of fiber are used excessively due to their desirable physical and chemical properties and their easier application. The main purpose of this research is to evaluate the characteristics of the asphalt mixture while using basalt fiber and glass fiber. In order to provide asphalt samples, these two types of fibers are used in different percentages. In this way, 42 samples (with different percentages of fiber and bitumen) were made using Marshal Hammer. In the next step, while constructing 63 asphalt samples using a gyratory device, then mix asphalt conventional tests include the determination of indirect tensile strength, moisture sensitivity test, and resilient modulus and creep tests performed. The results of this research indicate that using these two types of fibers increased the percentage of optimum bitumen and marshal resistance. At best, adding 0.1% glass fiber resulted in 13% increase in marshal resistance. Ultimately, ANFIS method was employed to predict the result of the experimental test and the possibility of using neural network method have been evaluated to predict the laboratory result.    &nbsp

Road Friction Virtual Sensing:A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper