205 research outputs found

    Characterization and prediction of permanent deformation properties of unbound granular materials for Pavement ME Design

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    The objective of this study is to characterize and predict the permanent deformation properties of unbound granular materials (UGMs) for Pavement ME Design. First, laboratory repeated load triaxial (RLT) tests are conducted on the UGMs from 11 quarries in Texas to measure the permanent strain curves. The shakedown theory is applied to evaluate the permanent deformation behavior of the selected UGMs. It is found that using Werkmeister's criteria to define the shakedown range boundaries is not suitable for the selected UGMs. Under this circumstance, new criteria are proposed to redefine the shakedown range boundaries for the flexible base materials in Texas. The new criteria are consistent with the current Texas flexible base specification in terms of aggregate classification. Second, the mechanistic-empirical design guide (MEPDG) model is used to determine the permanent deformation properties of the selected UGMs on the basis of the measured permanent strain curves. The determined permanent deformation properties are assigned as target values for the development of permanent deformation prediction models. Third, a series of performance-related base course properties are used to comprehensively characterize the UGMs, which include the dry density, moisture content, aggregate gradation, morphological properties, percent fines content, and methylene blue value. These performance-related base course properties are assigned as the inputs of the permanent deformation prediction models. Fourth, a multiple regression analysis is conducted to develop the prediction models for permanent deformation properties using these performance-related properties. The developed models are capable of accurately predicting the permanent deformation properties of UGMs. Compared to other prediction models (e.g., simple indicators-based models and Pavement ME Design models), the developed models have the highest prediction accuracy. It is also found that the Pavement ME model-predicted permanent strains are much lower than those measured from the RLT tests. This demonstrates that the current Pavement ME Design software substantially underestimates the rutting that occurs in base course. Finally, the developed prediction models are validated by comparing the predicted and measured permanent strains of other four base materials. The obtained R-squared value of 0.81 indicates that the developed models have a desirable accuracy in the prediction of permanent deformation properties of UGMs

    The development of correlations between HMA pavement performance and aggregate shape properties

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    The physical characteristics of aggregates (form, angularity, and texture) are known to affect the performance of hot mix asphalt (HMA) pavements. Efforts to develop relationships between these aggregate characteristics and aggregate performance in HMA pavements have been limited in the past due to inherent inaccuracies in the methods used to measure these characteristics. The recently developed Aggregate Imaging System (AIMS) offers an opportunity to accurately measure aggregate shape characteristics allowing them to be properly related to asphalt performance. This research focused on relating the aggregate characteristics of form, angularity, and texture measured using AIMS to laboratory performance measurements on a wide variety of HMA mixes. The performance of these mixes was evaluated in three projects carried out by the Federal Highway Administration (FHWA) and the Texas Transportation Institute (TTI). During this research, a database of the volumetric, performance, and aggregate shape measurements for mixes used in these projects was created. Statistical analysis was conducted to correlate HMA performance parameters to volumetric and aggregate shape characteristics. The results show the dominant effect that aggregate shape properties have on HMA performance

    International Center for Partnered Pavement Preservation (ICP3): First Year Progress Report

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    0-6878The Accelerating Innovation in Partnered Pavement Preservation project was initiated to promote and streamline research in the area of pavement preservation and to optimize the use of Texas Department of Transportation's (TxDOT\u2019s) research and implementation resources by fostering cooperation and collaboration with the US DOT Center for Highway Pavement Preservation (CHPP). CHPP is a research and innovation partnership lead by Michigan State University which members include: The University of Texas at Austin, The University of Illinois at Urbana-Champaign, The University of Minnesota, The University of Hawaii at Manoa and North Carolina A&T University. This preliminary progress report summarizes the work performed during the first five months of the project, from April to August 2015. During this period two task orders were developed and the corresponding work was planned and initiated. This report also presents the initial findings of these two task orders. The two task orders are: 1) Determination of Field Performance of Thin Overlays Relative to Alternative Preservation Techniques and 2) Quantification of Highway Pavement Surface Micro- and Macro-Texture

    Characterization of aggregate shape properties using a computer automated system

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    Shape, texture, and angularity are among the properties of aggregates that have a significant effect on the performance of hot-mix asphalt, hydraulic cement concrete, and unbound base and subbase layers. Consequently, there is a need to develop methods that can quantify aggregate shape properties rapidly and accurately. In this study, an improved version of the Aggregate Imaging System (AIMS) was developed to measure the shape characteristics of both fine and coarse aggregates. Improvements were made in the design of the hardware and software components of AIMS to enhance its operational characteristics, reduce human errors, and enhance the automation of test procedure. AIMS was compared against other test methods that have been used for measuring aggregate shape characteristics. The comparison was conducted based on statistical analysis of the accuracy, repeatability, reproducibility, cost, and operational characteristics (e.g. ease of use and interpretation of the results) of these tests. Aggregates that represent a wide range of geographic locations, rock type, and shape characteristics were used in this evaluation. The comparative analysis among the different test methods was conducted using the Analytical Hierarchy Process (AHP). AHP is a process of developing a numerical score to rank test methods based on how each method meets certain criteria of desirable characteristics. The outcomes of the AHP analysis clearly demonstrated the advantages of AIMS over other test methods as a unified system for measuring the shape characteristics of both fine and coarse aggregates. A new aggregate classification methodology based on the distribution of their shape characteristics was developed in this study. This methodology offers several advantages over current methods used in practice. It is based on the distribution of shape characteristics rather than average indices of these characteristics. The coarse aggregate form is determined based on three-dimensional analysis of particles. The fundamental gradient and wavelet methods are used to quantify angularity and surface texture, respectively. The classification methodology can be used for the development of aggregate shape specifications

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

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    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

    Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

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    Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

    Variability of gravel pavement roughness: an analysis of the impact on vehicle dynamic response and driving comfort

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    Gravel pavement has lower construction costs but poorer performance than asphalt surfaces on roads. It also emits dust and deforms under the impact of vehicle loads and ambient air factors; the resulting ripples and ruts constantly deepen, and therefore increase vehicle vibrations and fuel consumption, and reduce safe driving speed and comfort. In this study, existing pavement quality evaluation indexes are analysed, and a methodology for adapting them for roads with gravel pavement is proposed. We report the measured wave depth and length of gravel pavement profile using the straightedge method on a 160 m long road section at three stages of road utilization. The measured pavement elevation was processed according to ISO 8608, and the frequency response of a vehicle was investigated using simulations in MATLAB/Simulink. The international roughness index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provided the objective results of the IRI calculation on the flexible pavement due to the decreasing velocity of a vehicle’s unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement was explored, identifying specific driving safety and comfort cases. Finally, an increase in the dynamic load coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrated the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers. The main relevant objectives of this study are the adaptation of a road roughness indicator to gravel pavement, including the evaluation of vehicle dynamic responses at different speeds and pavement deterioration states

    Evaluation of pavement skid resistance using computational intelligence

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    Pavement micro-texture is affected by the aggregate characteristics contained within the surface. It is long desired to develop friction prediction models using pavement surface and aggregate textural properties. However, the development of such models has proven to be challenging because of two reasons: (1) The acquiring of complete and high quality pavement surface data for friction studies remains difficult. (2) No consistent and reliable methodologies and models have been developed for friction prediction and evaluation.The objective of this dissertation is to investigate the most influencing factors for pavement skid resistance, and develop reliable and consistent friction prediction models based on aggregate physical properties and pavement surface texture characteristics from three perspectives. The state-of-the-art 3D laser imaging technology, high speed texture profiler, and the continuous friction measurement equipment (CFME) - Grip Tester, are used in parallel in the field to collect 1-mm 3D pavement surface data, macro-texture profiles and pavement friction data respectively at highway speed for selected testing locations, while the newly developed portable ultra-high resolution 3D texture scanner (LS-40) is utilized in the laboratory to acquire both macro- and micro-texture characteristics of pavement surfaces, and the Aggregate Image Measurement System (AIMS) to analyze surface characteristics of aggregates before and after the Micro-Deval polishing process.Firstly, this study predicts pavement friction as a function of pavement surface and aggregate texture properties. Secondly, panel data analysis (PDA), which is able to investigate the differences of cross-sectional information, but also the time-series changes over time, is conducted to evaluate pavement skid resistance performance and identify the most influencing factors. Finally, inspired by the big success of deep learning in the field of image recognition and computer vision, a novel Deep Residual Network (ResNets) tailored for pavement friction prediction, named Friction-ResNets, is developed using pavement surface texture profiles as the inputs.This dissertation developed several novel friction prediction models that could assist in selecting the most effective PM treatments, and proper aggregates with desired texture characteristics for optimized skid resistance. This study also demonstrates the feasibility of replacing the contact based method for pavement friction evaluation with non-contact texture measurements
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