Journal of Transportation Engineering
Year:2018 | Volume:9 | Issue:SUPP |Papers Count:10

The most important part of a pavement management system is the pavement performance prediction model. The efficiency of maintenance and rehabilitation is dependent on the accuracy and validation of pavement performance prediction model. In family models, several pavement sections with similar properties and deterioration fall into a family. So, performance model is made for all pavements of a family. Modeling based on pavement family can provide high accuracy results with fastest and costless way and minimum data is needed. In this paper, two pavement families are defined in City of Sari streets including family 1 with high traffic loading and more asphalt thickness and family 2 with low traffic loading and less asphalt thickness. All arterial and other important streets are evaluated, and then pavement condition index (PCI) and pavement age is determined. For each pavement family a regression model is made and finally a third degree model is developed. Family 1 of pavements presents R2=0.90 and family 2 has a regression coefficient R2=0.84. In addition, for each family, the pavement performance is predicted by using a multi-layer perceptron neural network. In both families a regression coefficient can be seen around of R2=0.93. Models are made based on just one pavement evaluation, so we can define their accuracy as very good. High accuracy model is resulted by pavement family idea. The results show that combining between family model and artificial neural network (ANN) can provide more accurate prediction than regression method. So, this method is recommended for movement management system in earlier stages.

Pre-stressed mono-block concrete sleepers are one of the most widely used types of concrete sleepers in high-speed heavy-haul railways. These sleepers have the capacity of bearing heavy axial wheels load at high speeds simultaneously. Sleepers used in high-speed heavy-haul railways can carry up to 30 tons of axial wheel loading at the speed of 200 km/hr. The sleepers used in high-speed heavy-haul are made of pre-stressed concrete. In this paper, crack propagation of mode I in pre-stressed concrete sleepers for heavy-haul high-speed lines, is simulated by fracture mechanics. The plastic damage model is used to analyze parameters of fracture mechanics in finite element software ABAQUS. This model investigates non-linear damage growth in concrete (NLFM). A positive three-point bending load is applied to the rail seat of the sleeper numerical model with 6 different crack lengths (starting zero and 5 mm from to 45 mm in increments of 10mm and a width of 8 mm). Damage growth, crack length and crack mouth opening displacement (CMOD) are calculated in this study. The results for damage shapes show that the fracture starts from the notch place and continues as bending damage in the line with the primary notch, and then crack bifurcation and the ultimate failure occurs eventually. This analysis shows that the structural behavior of pre-stressed concrete sleepers using fracture mechanics model, simply can be predicted using damage growth, crack length and CMOD.

The pavement quality has a great impact on safety of road and convenience of driving and makes it essential to evaluate the asphalt performance to control its common failure such as moisture sensitivity. Although the amount of bitumen in asphalt mixtures is not high, it has a significant impact on mixture performance. So, when the performance of modified bitumen improved by additives, it would result in better performance of asphalt mixture. Evaluation of moisture sensitivity in modified asphalt mixture by adding Zycotherm and Evonic as bitumen modifiers has been studied in this research. Limestone graded number 4, bitumen with 60-70 penetration grade and two nanomaterial additives called Zycotherm and Evonic in two different percentage (0.1 and 0.3 weight percentage of bitumen) have been used to make the asphalt mixture samples in this study. In order to evaluate the effects of these two nanomaterials on moisture sensitivity of asphalt mixture, the indirect tensile strength test (modified Lotman test) and Texas boiling test has been performed. In addition, Marshal stability test, resilient modulus test and dynamic creep test have been done on both modified and unmodified asphalt mixture samples. A comparison between the test’s results of modified and unmodified mixtures shows a better sustainability of asphalt against moisture by using Zycotherm and Evonic as bitumen modifiers. The suitable portion of each nanomaterial additives are 0.1 percentage for Zycotherm and 0.3 percentage for Evonic. Moreover, the results show that the Zycotherm is more effective on improvement of moisture sensivity and performance of asphalt mixture.

Soil improvement a method is used for increasing bearing capacity of materials applied in pavement layers. Stabilization generally performed based on two methods chemically and mechanically. Currently geosynthetic materials are used for soil improvement in retaining walls and earth fall. The application of geogrid layers is one of the effective method in many engineering projects and pavement design for soil improvement. Increase of clay or silt content in sand in various percent can basically change geotechnical behavior of mixture. Main aim of this research is experimental study of georid layers locating and number of them effects on bearing capacity and shear strength of sandy and clayey soils from Barandouz area. In this study sandy soil mixed with clay in 25, 50 and 75%. The effect of geogrids on geotechnical properties of specimens studied in two positions: first a geogrid embedded in the middle of materials and second two geogrids by equal depth from each other used in soils. For evaluating geotechnical behavior of sand-clay mixtures California bearing ratio (CBR) and Direct shear tests performed based on ASTM in both dry and saturate conditions. Also, percent of swelling in fines content saturated soils assessed. Results showed that in unreinforced specimens with increasing fines content in bearing capacity and geotechnical properties decreased. Then, with embed a geogrid layer in middle part of specimens compared to two geogrid layers value of sand skeleton void ratio decreased and maximum dry unit weight increased and at that time bearing capacity and shear strength of materials in both dry and saturate condition improved. Also, swelling percent of fines content soils with embed a geogrid layer significantly as compared to an unreinforced decreased. Therefore, it is recommended application of one geogrid layer in sand-clay mixtures is optimum for design subgrade and sub base layers in pavement.

In this research, rutting and top-down cracking of asphaltic layer in composite pavement with RCC have been investigated. To this end, 3D models of pavement have been made in Finite Element software of ABAQUS. Performance of 5 different asphaltic mixtures has been evaluated and compared. In addition, the effects of using different geogrids in different depths of asphaltic layer on the rutting and top-down cracking have been investigated. The effects of asphalt surface and RCC base thickness on the rutting and top-down cracking has been studied. Results show that type of mixture in surface layer is significantly important in rutting and top-down fatigue cracking. In addition it has been found that placing the geogrid at the mid-depth of asphalt surface decreases the rut depth, and, pacing that at the bottom of asphaltic layer is not effective on the rut depth. The effectiveness of geogrid is found to be dependent on the stiffness of geogrid, type of mixture and thickness of asphaltic layer. Also, placing the geogrid in the mid-depth of asphaltic layer decreases the top-down fatigue life, and placing that at the bottom of asphaltic layer is not effective on the fatigue life. The results of analysis show that rut depth increases with increasing the thickness of asphaltic layer, but is independent of the thickness of RCC layer thickness. The maximum tensile strain at the surface of asphaltic layer is found to vary with the thickness of asphaltic surface and RCC layer. However, the variation does not follow a trend.

Although laying an asphalt concrete overlay on the old existing surface is a common method used for pavement improvement, weak bonding strength between the existing old layer and the new overlay is a concern that can decrease the pavement service life significantly. A common method for increasing bonding between pavement layers is using tack coat between pavement layers Therefore, this paper aims at evaluating the bonding strength between sand asphalt layer and RCC and considers different parameters including type of tack coat, tack coat dosage and the existing layer surface temperature. Four tack coat types including grade 60-70, modified grade 60-70 with crumb rubber, CSS and CRS emulsions were considered and in order to determine the most proper dosage of tack coat, 200, 400 and 600 gr/m2 tack coats were applied to the existing layer surface. The RCC layer surface temperatures were 0, 25 and 60 centigrade degrees. Bonding strength test was determined using LPDS test device on cylindrical specimens. Results showed that grade 60-70 modified with crumb rubber significantly increases bonding strength but shows weak bonding in low surface temperature. Bottom layer surface temperature (RCC layer), showed to have significant effect on bonding strength between layers. When the surface temperature is around zero degrees centigrade, bonding strength decreases significantly especially when high tack coat dosage is applied. Evaluating different amount of tack coat showed that the optimum dosage for CRS and CSS emulsion is 400 and 200 gr/m2 respectively. Increasing tack coat dosage for grade 60.70 and modified 60.70 to 600 gr/m2 showed great increase in bonding strength.

Road safety and convenience of driving has been greatly affected by pavement quality; so, it is essential to evaluate the asphalt performance and its common failure such as Fatigue. Nowadays, an increase in frequency of heavy load traffic causes premature fatigue distresses in asphalt pavements which leads to enhancement the costs of road maintenance and repairs. This makes the improvement of the properties of bitumen and asphalt mixtures inevitable. Although the amount of bitumen in asphalt mixtures is not high, it has a significant impact on mixture performance. Styrene-butadiene-styrene is known as one of the most used polymers to improve the properties of asphalt which has led researchers to use alternative materials with the same functional properties due to the lack of sufficient instability in bitumen and its high price. In this study, to find a suitable alternative for this polymer, a mixture of low-cost polymers of styrene-butadiene-rubber and polystyrene has been used with the ratio from 35 to 65 percentage of each combination. In this regard, styrene-butadiene-styrene and polymer blends has been added to the bitumen of the asphalt samples by 3 to 5 percentage. Moreover, 2 percent nanoclay has been mixed inorder to increase the stability of the polymers in bitumen. Thereafter, four-point bending beam and indirect stretch tests has been utilized to evaluate fatigue performance of samples. The results of this research shows that the polymer blends with mentioned composition can be used instead of unstable and expensive traditional SBS, especially when samples have more than 50 percentage styrene-butadiene-rubber in their compositions.

Evaluation of pavement performance is one of the most prominent assets in choosing the beneficial strategy for pavement management operations. In the past two decades, a considerable number of investigations have been carried out on developing automatic methods for distress rocognition all of which rely on the machine vision and image processing techniques. In the past few years multi-resolutional analysis methods, namely wavelet transform has provided a great tool for fast and accurate auto-detection of distresses. In the present study, a method has been proposed utilizing the wavelet transform method which can analyze the texture surface of pavement considering the longitudinal, transverse and diagonal textural structures as the key elements. In this paper, after performing the discrete wavelet transform and decomposing the image into frequency sub-bands using 4 different wavelet families, properties of sub-bands texture has been acquired (based on grey level co-occurrence matrix) and compared to the results acquired based on image texture analysis in spatial domain. Finally, the minimal Mahalanobis distance method was applied in order to categorize the acquired images into seven classes including alligator cracking, without distress, longitudinal cracking, transverse cracking, bleeding, patching and raveling. Based on the results of validation and evaluation of the classifiction performance it was observed that the distress image classification using image texture analysis in the transformation domain leads to the more accurate results in comparison to spatial domain. The mean accuracy of distress image classification in transformation domain is 67% while the accuracy rate in classification of distress data based on extraction of texture features in spatial domain is 49.76%. In case of transformation domain, although Daubechies 2 filter has a better sensitivity rate in discrimination of bleeding distress, in general, the Haar filter outperformed other utilized wavelets in recognition and classification of asphalt pavement surface distresses with 95% accuracy.

This research evaluates the effect of recycled polyethylene terephthalate (PET) fibers on the fatigue properties of hot mix asphalt. Based on micro-structural analysis, fiber contents were taken into account as equal to 0.5, 1.0, 1.5 and 2.0% by weight of bitumen. Fibers used with two lengths of 1 and 2 cm and an average diameter of 30 mm. For comparison, crumb PET with the particle size of 0.425-1.180 mm was used in two contents of 1 and 2% by weight of bitumen. Slab specimens of the reference and reinforced mixes were compacted by laboratory roller compactor to achieve 4±0.2 % air void. After that, the specimens were removed from the mold and sawed to obtain beam-shaped specimens. Then, the 4-point bending test was performed to evaluate the fatigue response of the modified and unmodified specimens by EN12697-24 standard. The fatigue tests were performed at 20oC and initial strain levels of 300, 500 and 700 microstrain using universal testing (UTM-14) apparatus. Based on the obtained results, the flexural stiffness of mixes containing both additives was lower than the reference mix. The reduction in the flexural stiffness continued as the fiber and crumb PET content increased. The fatigue life of fiber reinforced mixes was higher than the reference one. At initial strain of 300 microstrain, the most increase in the fatigue life of fiber reinforced mixes containing 1 and 2 cm fibers were equal to 149 and 177% of the reference mix, respectively. Also, at 700 microstrain the increases in the fatigue life of aforementioned mixes were 138 and 163% of the unmodified mix, respectively. The most increases in the fatigue life of the specimens containing 1 and 2% of the crumb PET were 148 and 163% of the reference one, respectively. Based on the obtained results and performed analyses, the optimum PET fiber and crumb PET contents were 1 and 2% by weight of bitumen, respectively.

Resilient modulus of subgrade soil is one of the most important parameters in terms of pavement analysis and design. This parameter is used for design of pavement structure based on both empirical (e.g. AASHTO 1993) and mechanistic-empirical methods (e.g. MEPDG). In order to determine resilient modulus, dynamic triaxial loading test should be conducted at different confining and deviator stresses on the soil samples and conducting such a test is very time consuming and costly. This paper aims to evaluate three hybrid neuro-computing methods including Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Support Vector Machine-Particle Swarm Optimization (SVM-PSO) and Adaptive neuro-Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO) for predicting resilient modulus of fine-grained soils. Input parameters in all of these models were considered as particles passing #200 sieve, liquid limit, plastic index, moisture content, optimum moisture content, degree of saturation, unconfined compression strength, confining stress, and deviator stress and output was assumed as resilient modulus of soil. Results show that ANN-PSO method has the highest accuracy in comparison with other methods. Coefficient of determination (R2) for ANN-PSO method was determined as 0.992 in case of overall dataset and in most cases the prediction error of resilient modulus using this method was less than 20%. Coefficient of determination for SVM-PSO method and ANFIS-PSO method were determined as 0.989 and 0.951, respectively. Results of this study also showed that the input parameter of particles passing #200 sieve has maximum influence on the resilient modulus of fine grained soil materials while the deviator stress has minimum impact on the resilient modulus of this type of materials.