Probabilistic characterisation of damage characteristic curve of asphalt concrete mixtures

ABSTRACT Due to its efficiency, viscoelastic continuum damage mechanics has been used for modelling asphalt concrete behaviour widely. In this approach, viscoelastic and damage response are used to obtain a damage characteristic curve. Under ideal conditions, these damage characteristic curves obtained under different loading conditions should collapse to a unique curve. Due to inherent variations during specimen fabrication and testing, significant scatter are found in damage characteristic curves even under well-controlled laboratory conditions. Owing to deterministic nature, present day viscoelastic continuum damage models fail to account this scatter in damage characteristic curves. This paper presents a probabilistic approach to describe the scatter in damage characteristic curves. Several specimens were tested for their viscoelastic properties and damage response. These test results were used to construct damage characteristic curves. The damage parameter values obtained at a particular normalised pseudostiffness values were fitted with normal, lognormal and Weibull distribution. It was observed that damage parameter values (at a particular normalised pseudostiffness) could be best described using Weibull distribution when compared to lognormal and normal distribution. Due to its flexibility, three-parameter Weibull distribution was found to fit better than two-parameter Weibull distribution. Further, significant differences were found between probabilistic damage characteristic curves developed in this research and conventional approach. The proposed methodology combines advantages of continuum damage mechanics as well as probabilistic approaches. These probabilistic fatigue curves can be conveniently used for reliability-based pavement design.

[1]  Kelvin C. P. Wang,et al.  Probabilistic Behavior of Pavements , 1994 .

[2]  Jo Sias Daniel Development of a simplified fatigue test and analysis procedure using a viscoelastic, continuum damage model and its implementation to WesTrack mixtures , 2001 .

[3]  D. Darling,et al.  A Test of Goodness of Fit , 1954 .

[4]  Jie Sun,et al.  Two-Parameter Weibull Distribution Theory Testing Analysis in Fatigue Life of Asphalt Mixture , 2011 .

[5]  Marko Nagode,et al.  A neural network approach to describing the scatter of S–N curves , 2006 .

[6]  G M Rowe,et al.  Performance of asphalt mixtures in the trapezoidal fatigue test , 1993 .

[7]  Matthew G. Karlaftis,et al.  Probabilistic Infrastructure Deterioration Models with Panel Data , 1997 .

[8]  Yongxiang Zhao,et al.  An approach for determining an appropriate assumed distribution of fatigue life under limited data , 2000, Reliab. Eng. Syst. Saf..

[9]  S. Caro,et al.  Probabilistic Analysis of Fracture in Asphalt Mixtures Caused by Moisture Damage , 2008 .

[10]  Shafeeq Ahmed,et al.  Sensitivity analysis of dynamic response and fatigue behaviour of various asphalt concrete mixtures , 2015 .

[11]  Susan L. Tighe,et al.  Guidelines for Probabilistic Pavement Life Cycle Cost Analysis , 2001 .

[12]  D. Little,et al.  USE OF DYNAMIC MECHANICAL ANALYSIS (DMA) TO EVALUATE THE FATIGUE AND HEALING POTENTIAL OF ASPHALT BINDERS IN SAND ASPHALT MIXTURES (WITH DISCUSSION AND CLOSURE) , 2002 .

[13]  Carl L Monismith,et al.  INVESTIGATION OF LABORATORY FATIGUE TESTING PROCEDURES FOR ASPHALT AGGREGATE MIXTURES , 1993 .

[14]  Robert L. Lytton,et al.  FATIGUE AND HEALING CHARACTERIZATION OF ASPHALT MIXTURES , 2003 .

[15]  C L Monismith INFLUENCE OF SHAPE, SIZE, AND SURFACE TEXTURE ON THE STIFFNESS AND FATIGUE RESPONSE OF ASPHALT MIXTURES , 1970 .

[16]  Y. Kim,et al.  CONTINUUM DAMAGE MECHANICS-BASED FATIGUE MODEL OF ASPHALT CONCRETE , 2000 .

[17]  P S Pell,et al.  ASPHALTIC ROAD MATERIALS IN FATIGUE , 1969 .

[18]  John T Harvey,et al.  Using the Three-Stage Weibull Equation and Tree-Based Model to Characterize the Mix Fatigue Damage Process , 2005 .

[19]  Surendra M. Gupta,et al.  A Neural Network Approach , 2020 .

[20]  Animesh Das,et al.  A re-visit to the development of fatigue and rutting equations used for asphalt pavement design , 2008 .

[21]  Lu Sun,et al.  Probabilistic Approaches for Pavement Fatigue Cracking Prediction based on Cumulative Damage using Miner’s Law , 2005 .

[22]  G. Zi,et al.  Probabilistic prognosis of fatigue crack growth for asphalt concretes , 2015 .

[23]  Richard Schapery,et al.  A theory of mechanical behavior of elastic media with growing damage and other changes in structure , 1990 .

[24]  Thomas W. Kennedy,et al.  FATIGUE AND REPEATED-LOAD ELASTIC CHARACTERISTICS OF INSERVICE ASPHALT-TREATED MATERIALS , 1975 .

[25]  Y. Richard Kim,et al.  LABORATORY EVALUATION OF FATIGUE DAMAGE AND HEALING OF ASPHALT MIXTURES , 2001 .

[26]  Y. Richard Kim,et al.  Improved calculation method of damage parameter in viscoelastic continuum damage model , 2010 .

[27]  Youngguk Seo,et al.  Dynamic Modulus Testing of Asphalt Concrete in Indirect Tension Mode , 2004 .

[28]  A. Swamy Evaluating mode of loading effect and laboratory fatigue performance of asphalt concrete using viscoelastic continuum damage mechanics , 2011 .

[29]  L. Kachanov,et al.  Rupture Time Under Creep Conditions , 1999 .

[30]  Mihai. Rafiroiu Structural design of asphalt pavements , 2018, Road Engineering for Development.

[31]  K P George,et al.  THICKNESS DESIGN FOR FLEXIBLE PAVEMENT: A PROBABILISTIC APPROACH , 1986 .

[32]  Aravind Krishna Swamy,et al.  Development of probabilistic fatigue curve for asphalt concrete based on viscoelastic continuum damage mechanics , 2016 .

[33]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[34]  Silvia Caro,et al.  Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements , 2014 .

[35]  Nenad Gucunski,et al.  Comparative Study of Static and Dynamic Falling Weight Deflectometer Back-Calculations Using Probabilistic Approach , 2010 .

[36]  Y R Kim,et al.  INTERCONVERSION BETWEEN RELAXATION MODULUS AND CREEP COMPLIANCE FOR VISCOELASTIC SOLIDS , 1999 .

[37]  M. Guddati,et al.  Development of a failure criterion for asphalt mixtures under fatigue loading , 2013 .

[38]  Ghassan R. Chehab,et al.  Probabilistic Modeling of Dynamic Modulus Master Curves for Hot-Mix Asphalt Mixtures , 2015 .

[39]  Richard Schapery Correspondence principles and a generalizedJ integral for large deformation and fracture analysis of viscoelastic media , 1984 .

[40]  Matija Fajdiga,et al.  Joint estimation of E–N curves and their scatter using evolutionary algorithms , 2013 .

[41]  H. Christopher Frey,et al.  Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs , 1999 .

[42]  Rafiqul A. Tarefder,et al.  Effects of recycled asphalt pavements on the fatigue life of asphalt under different strain levels and loading frequencies , 2015 .

[43]  J. Daniel,et al.  Effect of Mode of Loading on Viscoelastic and Damage Properties of Asphalt Concrete , 2012 .

[44]  Okan Sirin,et al.  Probabilistic analysis of fatigue life for asphalt mixtures using the viscoelastic continuum damage approach , 2016 .

[45]  M. Emin Kutay,et al.  Conventional and Viscoelastic Continuum Damage (VECD)-Based Fatigue Analysis of Polymer Modified Asphalt Pavements (With Discussion) , 2008 .

[46]  John T Harvey,et al.  Probabilistic Approach to Life-Cycle Cost Analysis of Preventive Maintenance Strategies on Flexible Pavements , 2012 .

[47]  Y. Richard Kim,et al.  Fatigue Performance Prediction of North Carolina Mixtures Using the Simplified Viscoelastic Continuum Damage Model , 2010 .