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

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994850 ◽

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.

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Development of Probabilistic Viscoelastic Continuum Damage Model for Asphalt Concrete

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119838980 ◽

2019 ◽

Vol 2673 (5) ◽

pp. 285-298 ◽

Cited By ~ 2

Author(s):

Hussein Kassem ◽

Ghassan Chehab ◽

Shadi Najjar

Keyword(s):

Asphalt Concrete ◽

Complex Modulus ◽

Experimental Testing ◽

Damage Model ◽

Material Model ◽

Continuum Damage ◽

Characteristic Curves ◽

Continuum Damage Model ◽

Viscoelastic Continuum Damage ◽

Damage Characteristic

The main objective of this paper is to develop a realistic probabilistic framework for characterization of different types of asphalt concrete using advanced material modeling. The adopted methodology builds on and enhances a viscoelastic continuum damage (VECD) material model by utilizing a suite of associated experimental testing protocols and incorporating the uncertainties associated with the different material properties. The modeled uncertainties address the variabilities and errors associated with the linear viscoelastic (LVE) functions achieved from the complex modulus test and damage characteristic curves obtained from constant crosshead rate testing. A probabilistic scheme using First Order approximations and Monte Carlo simulations is developed to characterize the inherent uncertainty of each of the LVE functions over the time domain of their mastercurves. For damage characteristic curves, the uncertainty in normalized pseudostiffness increases as the level of damage becomes larger. The uncertainties of LVE properties are propagated along with those of C versus stress curves to yield a probabilistic viscoelastic continuum damage model (P-VECD). The P-VECD not only predicts the average viscoelastic response to a given loading input, but it can also provide its distribution, which is essential for a reliability-based pavement design.

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Uncertainty Quantification of Simplified Viscoelastic Continuum Damage Fatigue Model using the Bayesian Inference-Based Markov Chain Monte Carlo Method

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120910149 ◽

2020 ◽

Vol 2674 (4) ◽

pp. 247-260 ◽

Cited By ~ 3

Author(s):

Jing Ding ◽

Yizhuang David Wang ◽

Saqib Gulzar ◽

Youngsoo Richard Kim ◽

B. Shane Underwood

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Monte Carlo Method ◽

Asphalt Concrete ◽

Dynamic Modulus ◽

Cyclic Fatigue ◽

Continuum Damage ◽

Viscoelastic Continuum Damage ◽

Damage Characterization ◽

The Difference

The simplified viscoelastic continuum damage model (S-VECD) has been widely accepted as a computationally efficient and a rigorous mechanistic model to predict the fatigue resistance of asphalt concrete. It operates in a deterministic framework, but in actual practice, there are multiple sources of uncertainty such as specimen preparation errors and measurement errors which need to be probabilistically characterized. In this study, a Bayesian inference-based Markov Chain Monte Carlo method is used to quantify the uncertainty in the S-VECD model. The dynamic modulus and cyclic fatigue test data from 32 specimens are used for parameter estimation and predictive envelope calculation of the dynamic modulus, damage characterization and failure criterion model. These parameter distributions are then propagated to quantify the uncertainty in fatigue prediction. The predictive envelope for each model is further used to analyze the decrease in variance with the increase in the number of replicates. Finally, the proposed methodology is implemented to compare three asphalt concrete mixtures from standard testing. The major findings of this study are: (1) the parameters in the dynamic modulus and damage characterization model have relatively strong correlation which indicates the necessity of Bayesian techniques; (2) the uncertainty of the damage characteristic curve for a single specimen propagated from parameter uncertainties of the dynamic modulus model is negligible compared to the difference in the replicates; (3) four replicates of the cyclic fatigue test are recommended considering the balance between the uncertainty of fatigue prediction and the testing efficiency; and (4) more replicates are needed to confidently detect the difference between different mixtures if their fatigue performance is close.

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

TRANSPORTES ◽

10.14295/transportes.v28i2.2163 ◽

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.

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Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽

10.3390/diagnostics11061127 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1127

Author(s):

Ji Hyung Nam ◽

Dong Jun Oh ◽

Sumin Lee ◽

Hyun Joo Song ◽

Yun Jeong Lim

Keyword(s):

Deep Learning ◽

Small Bowel ◽

Scoring System ◽

Operating Characteristic ◽

Clinical Evidence ◽

Learning Algorithm ◽

Characteristic Curve ◽

External Validation ◽

Test Results ◽

Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

Statistical Methods in Medical Research ◽

10.1177/0962280221995956 ◽

2021 ◽

pp. 096228022199595

Author(s):

Yalda Zarnegarnia ◽

Shari Messinger

Keyword(s):

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Case Control ◽

Paired Data ◽

Characteristic Curves ◽

Receiver Operating Characteristic Curves ◽

Operating Characteristic Curve ◽

Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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Derivation of water flooding characteristic curve for offshore low-amplitude structural reservoir with strong bottom water

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01240-z ◽

2021 ◽

Author(s):

Ying-xian Liu ◽

Jie Tan ◽

Hui Cai ◽

Yan-lai Li ◽

Chun-yan Liu

Keyword(s):

Bottom Water ◽

Characteristic Curve ◽

Calculated Data ◽

Oil Production ◽

Water Flooding ◽

Characteristic Curves ◽

Recoverable Reserves ◽

Curve Method ◽

Water Cut ◽

Low Amplitude

AbstractThe water flooding characteristic curve method is one of the essential techniques to predict recoverable reserves. However, the recoverable reserves indicated by the existing water flooding characteristic curves of low-amplitude reservoirs with strong bottom water increase gradually, and the current local recovery degree of some areas has exceeded the predicted recovery rate. The applicability of the existing water flooding characteristic curves in low-amplitude reservoirs with strong bottom water is lacking, which affects the accurate prediction of development performance. By analyzing the derivation process of the conventional water flooding characteristic curve method, this manuscript finds out the reasons for the poor applicability of the existing water flooding characteristic curve in low-amplitude reservoir with strong bottom water and corrects the existing water flooding characteristic curve according to the actual situation of the oilfield and obtains the improvement method of water flooding characteristic curve in low-amplitude reservoir with strong bottom water. After correction, the correlation coefficient between $$\frac{{k_{ro} }}{{k_{rw} }}$$ k ro k rw and $$S_{w}$$ S w is 95.92%. According to the comparison between the actual data and the calculated data, in 2021/3, the actual water cut is 97.29%, the water cut predicted by the formula is 97.27%, the actual cumulative oil production is 31.19 × 104t, and the predicted cumulative oil production is 31.31 × 104t. The predicted value is consistent with the actual value. It provides a more reliable method for predicting low-amplitude reservoirs' recoverable ability with strong bottom water and guides the oilfield's subsequent decision-making.

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