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Author: Kasthurirangan Gopalakrishnan Publisher: ISBN: Category : Asphalt Languages : en Pages : 65
Book Description
The asphalt concrete (AC) dynamic modulus (\E*\) is a key design parameter in mechanistic-based pavement design methodologies such as the American Association of State Highway and Transportation Officials (AASHTO) MEPDG/Pavement-ME Design. The objective of this feasibility study was to develop frameworks for predicting the AC \E*\ master curve from falling weight deflectometer (FWD) deflection-time history data collected by the Iowa Department of Transportation (Iowa DOT). A neural networks (NN) methodology was developed based on a synthetically generated viscoelastic forward solutions database to predict AC relaxation modulus (E(t)) master curve coefficients from FWD deflection-time history data. According to the theory of viscoelasticity, if AC relaxation modulus, E(t), is known, \E*\ can be calculated (and vice versa) through numerical inter-conversion procedures. Several case studies focusing on full-depth AC pavements were conducted to isolate potential backcalculation issues that are only related to the modulus master curve of the AC layer. For the proof-of-concept demonstration, a comprehensive full-depth AC analysis was carried out through 10,000 batch simulations using a viscoelastic forward analysis program. Anomalies were detected in the comprehensive raw synthetic database and were eliminated through imposition of certain constraints involving the sigmoid master curve coefficients. The surrogate forward modeling results showed that NNs are able to predict deflection-time histories from E(t) master curve coefficients and other layer properties very well. The NN inverse modeling results demonstrated the potential of NNs to backcalculate the E(t) master curve coefficients from single-drop FWD deflection-time history data, although the current prediction accuracies are not sufficient to recommend these models for practical implementation. Considering the complex nature of the problem investigated with many uncertainties involved, including the possible presence of dynamics during FWD testing (related to the presence and depth of stiff layer, inertial and wave propagation effects, etc.), the limitations of current FWD technology (integration errors, truncation issues, etc.), and the need for a rapid and simplified approach for routine implementation, future research recommendations have been provided making a strong case for an expanded research study.
Author: Kasthurirangan Gopalakrishnan Publisher: ISBN: Category : Asphalt Languages : en Pages : 65
Book Description
The asphalt concrete (AC) dynamic modulus (\E*\) is a key design parameter in mechanistic-based pavement design methodologies such as the American Association of State Highway and Transportation Officials (AASHTO) MEPDG/Pavement-ME Design. The objective of this feasibility study was to develop frameworks for predicting the AC \E*\ master curve from falling weight deflectometer (FWD) deflection-time history data collected by the Iowa Department of Transportation (Iowa DOT). A neural networks (NN) methodology was developed based on a synthetically generated viscoelastic forward solutions database to predict AC relaxation modulus (E(t)) master curve coefficients from FWD deflection-time history data. According to the theory of viscoelasticity, if AC relaxation modulus, E(t), is known, \E*\ can be calculated (and vice versa) through numerical inter-conversion procedures. Several case studies focusing on full-depth AC pavements were conducted to isolate potential backcalculation issues that are only related to the modulus master curve of the AC layer. For the proof-of-concept demonstration, a comprehensive full-depth AC analysis was carried out through 10,000 batch simulations using a viscoelastic forward analysis program. Anomalies were detected in the comprehensive raw synthetic database and were eliminated through imposition of certain constraints involving the sigmoid master curve coefficients. The surrogate forward modeling results showed that NNs are able to predict deflection-time histories from E(t) master curve coefficients and other layer properties very well. The NN inverse modeling results demonstrated the potential of NNs to backcalculate the E(t) master curve coefficients from single-drop FWD deflection-time history data, although the current prediction accuracies are not sufficient to recommend these models for practical implementation. Considering the complex nature of the problem investigated with many uncertainties involved, including the possible presence of dynamics during FWD testing (related to the presence and depth of stiff layer, inertial and wave propagation effects, etc.), the limitations of current FWD technology (integration errors, truncation issues, etc.), and the need for a rapid and simplified approach for routine implementation, future research recommendations have been provided making a strong case for an expanded research study.
Author: Amara Loulizi Publisher: ISBN: Category : Pavements, Asphalt Languages : en Pages : 32
Book Description
This project evaluated the procedures proposed by the Mechanistic-Empirical Pavement Design Guide (MEPDG) to characterize existing hot-mix asphalt (HMA) layers for rehabilitation purposes. Thirty-three cores were extracted from nine sites in Virginia to measure their dynamic moduli in the lab. Falling-weight deflectometer (FWD) testing was performed at the sites because the backcalculated moduli are needed for the Level 1 procedure. The resilient modulus was also measured in the lab because it is needed for the Level 2 procedure. A visual pavement rating was performed based on pavement condition because it is needed for the Level 3 procedure. The selected cores were tested for their bulk densities (Gmb) using the AASHTO T166 procedure and then for their dynamic modulus in accordance with the AASHTO TP62-03 standard test method. Then the cores were broken down and tested for their maximum theoretical specific gravity (Gmm) using the AASHTO T-209 procedure. Finally an ignition test was performed to find the percentage of binder and to reclaim the aggregate for gradation analysis. Volumetric properties were then calculated and used as input for the Witczak dynamic modulus prediction equations to find what the MEPDG calls the undamaged master curve of the HMA layer. The FWD data, resilient modulus data, and pavement rating were used to find the damaged master curve of the HMA layer as suggested for input Levels 1, 2, and 3, respectively. It was found that the resilient modulus data needed for a Level 2 type of analysis do not represent the entire HMA layer thickness, and therefore it was recommended that this analysis should not be performed by VDOT when implementing the design guide. The use of Level 1 data is recommended because FWD testing appears to be the only procedure investigated that can measure the overall condition of the entire HMA layer.
Author: Erol Tutumluer Publisher: Springer Nature ISBN: 9811523495 Category : Science Languages : en Pages : 1030
Book Description
This volume presents papers from the 8th International Symposium on Environmental Vibration and Transportation Geodynamics (ISEV2018). It covers the latest advances in the areas of environmental vibrations, and its impact on dynamic vehicular loading, transportation infrastructures and the built environment. This volume will be of interest to policy-makers and researchers in academia, industry and government.
Author: Y. Richard Kim Publisher: CRC Press ISBN: 1315736756 Category : Technology & Engineering Languages : en Pages : 1966
Book Description
Asphalt Pavements contains the proceedings of the International Conference on Asphalt Pavements (Raleigh, North Carolina, USA, 1-5 June 2014), and discusses recent advances in theory and practice in asphalt materials and pavements. The contributions cover a wide range of topics:- Environmental protection and socio-economic impacts- Additives and mo
Author: Nam H. Tran Publisher: ISBN: Category : Asphalt concrete Languages : en Pages : 354
Book Description
Mechanistic-empirical flexible pavement design procedures proposed for use within the 2002 Design Guide require the input of the dynamic modulus (E*) of hot-mix asphalt concrete. In addition, the E* test has been proposed as a "simple performance test" for use in mixture design and construction quality control. The objective of this study included conducting the dynamic modulus test, evaluating the accuracy/variability of test results, and constructing master curves for the mixtures tested. The hotmix asphalt mixes tested in this research are typically used for pavement construction in Arkansas, and binder content and air voids were varied to simulate typical construction variability. The analysis showed that the variability of the average dynamic modulus for each set of four replicates was acceptable. Since the dynamic modulus tests were run at intermediate temperatures in this study, a modified procedure, using Arrhenius and power functions, was employed to construct the master curves. Based on the master curves, the effects of aggregate size, binder content, and air voids on the tested asphalt mixtures were evaluated and determined to be consistent and reasonable. The testing procedure and results of this study were recommended for use in a new project to characterize the stiffness of Arkansas mixtures to prepare input data for the proposed 2002 Design Guide.
Author: Andreas Loizos Publisher: CRC Press ISBN: 1351585789 Category : Technology & Engineering Languages : en Pages : 3209
Book Description
Bearing Capacity of Roads, Railways and Airfields includes the contributions to the 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields (BCRRA 2017, 28-30 June 2017, Athens, Greece). The papers cover aspects related to materials, laboratory testing, design, construction, maintenance and management systems of transport infrastructure, and focus on roads, railways and airfields. Additional aspects that concern new materials and characterization, alternative rehabilitation techniques, technological advances as well as pavement and railway track substructure sustainability are included. The contributions discuss new concepts and innovative solutions, and are concentrated but not limited on the following topics: · Unbound aggregate materials and soil properties · Bound materials characteritics, mechanical properties and testing · Effect of traffic loading · In-situ measurements techniques and monitoring · Structural evaluation · Pavement serviceability condition · Rehabilitation and maintenance issues · Geophysical assessment · Stabilization and reinforcement · Performance modeling · Environmental challenges · Life cycle assessment and sustainability Bearing Capacity of Roads, Railways and Airfields is essential reading for academics and professionals involved or interested in transport infrastructure systems, in particular roads, railways and airfields.
Author: Dharamveer Singh Publisher: ISBN: Category : Crack and seat treatment Languages : en Pages : 11
Book Description
In this paper, a procedure for estimation of effective modulus of a multilayered hot mix asphalt (HMA) pavement using intelligent compaction (IC) is investigated. The study is conducted during the construction of an interstate highway (I-35) in Norman, OK. A complete coverage of the level of compaction of each of the asphalt pavement layers was recorded using the intelligent asphalt compaction analyzer (IACA). The collected IACA data allow determination of the level of compaction (density) at any selected location, for each layer, and provided a set of global positioning system (GPS) coordinates. Calibration procedures have previously been tested and validated by the authors to estimate the density of different types of pavements from IACA data. In this paper, a different calibration procedure is used to measure the dynamic modulus instead of the density of a pavement using IACA. Considering the IACA estimated density, the dynamic modulus of each of the selected locations for an individual pavement layer was measured from laboratory developed master curves. Thereafter, an effective modulus of the three-layer pavement system was calculated for all of the selected locations using Odemark's method. The proposed technique was verified by conducting falling-weight deflectometer (FWD) tests at these selected locations. Analyses of the results show that the proposed intelligent compaction technique may be promising in estimating the effective modulus of the pavement layers in a non-destructive manner. In addition, the Witczak model was used to estimate moduli of each of the pavement layers. The comparison of the Witczak model with FWD revealed that the model over-predicted the modulus significantly.
Author: Kasthurirangan Gopalakrishnan
Author: Kasthurirangan Gopalakrishnan
Author: Amara Loulizi
Author: Erol Tutumluer
Author: Edmund Surette
Author: Y. Richard Kim
Author: Nam H. Tran
Author: Andreas Loizos
Author: Thomas Bennert
Author: Y. Richard Kim
Author: Dharamveer Singh