scholarly journals Algorithm for determination of the damage characteristic (C-S) curve of asphalt mixtures

TRANSPORTES ◽  
2020 ◽  
Vol 28 (2) ◽  
pp. 100-110
Author(s):  
Francisco José Pereira De Almeida ◽  
Suyanne Costa Silva ◽  
Jorge Barbosa Soares ◽  
Evandro Parente Junior
Keyword(s):  
Characteristic Curve ◽  
Subsequent Treatment ◽  
Asphalt Mixtures ◽  
Direct Tension ◽  
Viscoelastic Continuum Damage ◽  
Tension Tests ◽  
Strain Testing ◽  
S Curve ◽  
Damage Characteristic

This paper presents the algorithm for calculating the damage characteristic curve obtained in direct tension tests taking into account sinusoidal controlled strain loading. The Viscoelastic Continuum Damage formulation is presented in a summarized form for the algorithm, in which the pseudo strain, at the instants associated to the observed stress, is calculated using the expression of the linear viscoelasticity stress under controlled strain testing. This facilitates subsequent treatment of the data to obtain the  vs.  curve. The proposed algorithm is simple to understand and easy to implement computationally. The algorithm was validated with the results of fatigue test simulations in three mixtures, which have indicated its potential.

Use of Mechanistic Models to Investigate Fatigue Performance of Asphalt Mixtures

2015 ◽  
Vol 2507 (1) ◽  
pp. 108-119 ◽  
Author(s):  
Jong-Sub Lee ◽  
Nelson Gibson ◽  
Y. Richard Kim
Keyword(s):  
Damage Model ◽  
Asphalt Mixtures ◽  
Fatigue Performance ◽  
Void Content ◽  
Continuum Damage ◽  
Direct Tension ◽  
Air Voids ◽  
Viscoelastic Continuum Damage ◽  
Air Void ◽  
Air Void Content

Effects of design air void contents, design voids in mineral aggregate (VMA), and in-place air voids on the fatigue performance of asphalt mixtures were investigated with mechanistic analyses based on the viscoelastic continuum damage (VECD) analyses and the mechanistic–empirical pavement analysis using the AASHTOWare Pavement ME Design program. The VECD analyses included the simplified viscoelastic continuum damage model at the material level and two structural models: ( a) layered viscoelastic analysis and ( b) layered viscoelastic pavement analysis for critical distresses. The mix design of a 2013 accelerated loading facility test lane was selected to develop the volumetric mix designs with the design air voids of 3%, 4%, and 5%, design VMAs of 13%, 14%, and 15%, and in-place air void contents of 5%, 7%, and 9% with the Bailey method. Dynamic modulus and direct tension cyclic fatigue tests were performed in accordance with the AASHTO TP 107 procedure. The test results showed that the linear viscoelastic property was affected by the design VMA, design air void content, and in-place air void content in order of sensitivity. Also, the damage states at failure determined from the damage characteristic curves and the mechanistic fatigue predictions had consistent trends as observed for the design VMA, in-place air void, and design air void content in rank of sensitivity. Finally, the design VMA, in-place air void, and design air void parameters were found to be sensitive in the mechanistic analyses, whereas the parameter that was most sensitive in the pavement mechanistic–empirical analysis was the in-place air void content.

Use of Resampling Method to Construct Variance Index and Repeatability Limit of Damage Characteristic Curve

2021 ◽  
pp. 036119812199485
Author(s):  
Jing Ding ◽  
Kangjin Caleb Lee ◽  
Cassie Castorena ◽  
Youngsoo Richard Kim ◽  
B. Shane Underwood
Keyword(s):  
Hypothesis Test ◽  
Characteristic Curve ◽  
Damage Model ◽  
Expert Judgment ◽  
Cyclic Fatigue ◽  
Viscoelastic Continuum Damage ◽  
Resampling Method ◽  
Repeatability Limit ◽  
Damage Characteristic ◽  
Variance Index

The simplified viscoelastic continuum damage model has been widely accepted as a tool to predict fatigue performance of asphalt concrete. One key component in the model is the damage characteristic curve that results from a cyclic fatigue test. This curve characterizes the relationship between material integrity (stiffness) and the level of damage in the material. As with any experimental measurement, it is important to know and quantify the variability of the damage curve, but traditional statistical methods are ill-suited for experiments that yield functional data as opposed to univariate data. In this study, a variance index of the damage characteristic curve is first proposed and compared with the expert judgment of the variance of a set of nine different asphalt mixtures. Then, an example analysis for establishing the repeatability limit of a specific mixture as the application of the variance index is presented using the resampling method and hypothesis test. The major findings are as follows: 1) the proposed variance index can match the expert judgment of variability; 2) the shape of the damage characteristic curve can affect the performance of the variance index; 3) the resampling method and hypothesis test can be applied to flag inconsistent data in multi-user or multi-laboratory results; and 4) the resampling method can also be used to construct the repeatability limit of the variance index.

Improving the Reliability of Damage Characteristic Curves in the Simplified Viscoelastic Continuum Damage Model

2018 ◽  
Vol 2672 (28) ◽  
pp. 493-502 ◽  
Author(s):  
Kangjin Lee ◽  
Cassie Castorena ◽  
Y. Richard Kim
Keyword(s):  
Characteristic Curve ◽  
Cyclic Fatigue ◽  
Pavement Performance ◽  
Continuum Damage ◽  
Test Results ◽  
Loading History ◽  
Characteristic Curves ◽  
Viscoelastic Continuum Damage ◽  
Performance Predictions ◽  
Damage Characteristic

One of the major advantages of the cyclic fatigue test (AASHTO TP 107) is that the results can be used to calibrate the Simplified Viscoelastic Continuum Damage (S-VECD) model, which is used for mechanistic pavement performance predictions. The crux of the S-VECD model is the damage characteristic curve, which has been shown to be independent of mode of loading, loading history, and temperature. Consequently, a model can be fitted to the damage characteristic curve and used to predict the damage response for any given loading history of interest using limited test results. AASHTO TP 107 currently lacks a specific procedure for fitting a model to the damage characteristic curve and evaluating the repeatability of test replicates. In this study, a robust and practical method is proposed for fitting a power law model to the damage characteristic curve. The proposed fitting method was verified using cyclic fatigue test results of 19 mixtures sourced from the United States, Canada, and South Korea. In addition, a means to evaluate the specimen-to-specimen variability of damage characteristic curves using a shape factor is proposed. Thresholds for acceptable variability in the shape factor were derived using confidence interval analysis and verified through FlexPAVE™ pavement performance predictions. The findings of this study can be used to improve the reliability of the damage characteristic curves derived from cyclic fatigue tests for pavement performance predictions.

scholarly journals Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 786
Author(s):  
Daniel M. Lang ◽  
Jan C. Peeken ◽  
Stephanie E. Combs ◽  
Jan J. Wilkens ◽  
Stefan Bartzsch
Keyword(s):  
Deep Learning ◽  
Cancer Patients ◽  
Oropharyngeal Cancer ◽  
Characteristic Curve ◽  
Learning Models ◽  
Convolutional Network ◽  
Hpv Status ◽  
3D Information ◽  
External Test

Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

Watertightness, cracking resistance, and self-healing of asphalt concrete used as a water barrier in dams

2013 ◽  
Vol 50 (3) ◽  
pp. 275-287 ◽  
Author(s):  
Yingbo Zhang ◽  
Kaare Höeg ◽  
Weibiao Wang ◽  
Yue Zhu
Keyword(s):  
Asphalt Concrete ◽  
Leakage Rate ◽  
Strain Rates ◽  
Self Healing ◽  
Test Results ◽  
Direct Tension ◽  
Coefficient Of Permeability ◽  
Tension Tests ◽  
Laboratory Test Results ◽  
Cracked Specimens

The coefficient of permeability of hydraulic asphalt concrete is in the range 10−8–10−10 cm/s. Laboratory test results show that triaxial specimens in axial compression can undergo axial strains up to 18% without any significant increase in permeability until approaching the compressive strength. For temperatures between 5 and 20 °C and strain rates between 2 × 10−3%/s and 5 × 10−3%/s, conventional hydraulic asphalt concrete can tolerate 1%–3% tensile strains before cracking in direct tension tests and strains up to 3%–4% in bending. At 20 °C the tensile and bending strains at cracking are 2–4 times higher than those at 0 °C, and at −20 °C they are approximately 0.2% and 0.8%, respectively. Asphalt concrete possesses pronounced crack self-healing properties. In the experiments, the crack leakage rate dropped 1–4 orders of magnitude within a few hours and the cracked specimens regained 55% of the intact tensile strength after only 1 day of self-healing. In summary, the comprehensive series of laboratory tests documents that asphalt concrete has characteristics that make the material extremely well suited for use in impervious barriers in dams, and the test results reported herein can be of great use in barrier design.

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