Variability evaluation of gradation for asphalt mixture in asphalt pavement construction

Abstract Variability of gradation for asphalt mixture is always the difficulty faced by the control of pavement construction, involving the pavement performance. The main purpose of this study is to improve the efficiency of detection for gradation variation and clarify the relationship between gradation variation and construction process. The variability of gradation for superpave asphalt mixtures was investigated with nominal maximum aggregate size (NMAS) of 13 mm, 20 mm, and 25 mm in construction. The correlation between the passing percentages at each sieve size and performance of asphalt mixture was first established by coupling grey relational analysis and entropy weight method. A digital image processing technology with multi-thresholds segmentation (DIP-MTS) was developed and verified through the experiment comparison. An approach to reversely calculate the gradation of cold aggregate was established. The mixture gradations during various construction stages were compared. Results illustrate that the passing percentages of sieves with NMAS, 4.75 mm, and 0.075 mm have a significant influence on the pavement performance and should be paid more attention during construction. The detection results of DIP-MTS are closer to the experimental value, and this technology is capable of detecting gradation for moving aggregate. The reverse calculation approach is proved feasible. Gradation variation in transportation is more severe than other construction processes.

[1]  L. Weidong,et al.  Numerical Simulation of Motion Rules of Coarse Aggregates in the Compaction Process , 2016 .

[2]  Chao Xing,et al.  Gradation measurement of asphalt mixture by X-Ray CT images and digital image processing methods , 2019, Measurement.

[3]  Hao Chen,et al.  Aggregate gradation theory, design and its impact on asphalt pavement performance: a review , 2019 .

[4]  Ma Tao.  Effect Factor Analysis of Asphalt Mixture Rut-resistance Property with Grey Relation Entropy Method , 2008 .

[5]  Rui Li,et al.  Evaluation methods and indexes of morphological characteristics of coarse aggregates for road materials: A comprehensive review , 2019, Journal of Traffic and Transportation Engineering (English Edition).

[6]  John M Kemeny,et al.  Block size distribution of in situ rock masses using digital image processing of drill core , 1997 .

[7]  Jiu-peng Zhang,et al.  Evaluation of aggregate gradation and distributing homogeneity based on the images of asphalt mixture , 2017 .

[8]  Andreas Loizos,et al.  Evaluation of the effects of gyratory and field compaction on asphalt mix internal structure , 2016 .

[9]  Andy Collop,et al.  The effect of asphalt mixture gradation and compaction energy on aggregate degradation , 2008 .

[10]  M. Oeser,et al.  Study on interfacial debonding between bitumen and aggregate based on micromechanical damage model , 2020, International Journal of Pavement Engineering.

[11]  K. Vislavičius,et al.  Effect of reclaimed asphalt pavement gradation variation on the homogeneity of recycled hot-mix asphalt , 2013 .

[12]  Changfa Ai,et al.  Effect of gradation segregation on low-temperature crack resistance of asphalt pavement using 3D DEM , 2021 .

[13]  I. Bessa,et al.  Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations , 2012 .

[14]  Zhanping You,et al.  Investigation of asphalt mixture internal structure consistency in accelerated discrete element models , 2020 .

[16]  Yanshun Jia,et al.  Reliability assessment of flexural fatigue failure of asphalt mixture: A new perspective , 2020 .

[17]  Peter E. Sebaaly,et al.  IMPACT OF CONSTRUCTION VARIABILITY ON PAVEMENT PERFORMANCE , 2004 .

[18]  Yong Li Ren.  Gradation Segregation Control in Asphalt Pavement Construction , 2010 .

[19]  Y. Zhang,et al.  Influence of gradation on asphalt mix rutting resistance measured by Hamburg Wheel Tracking test , 2020 .

[20]  Jiu-peng Zhang,et al.  Micromechanical model for asphalt mixture coupling inter-particle effect and imperfect interface , 2017 .

[21]  Peter E. Sebaaly,et al.  A Laboratory Study of Construction Variability Impacts on Fatigue and Thermal Cracking Resistance of HMA Mixtures , 2006 .

[22]  Suhana Koting,et al.  Effects of Aggregate Gradation on the Physical Properties of Semiflexible Pavement , 2014 .

[23]  Jiu-peng Zhang,et al.  Characterizing Heterogeneity of Asphalt Mixture Based on Aggregate Particles Movements , 2018, Iranian Journal of Science and Technology, Transactions of Civil Engineering.

[24]  Zeyu Zhang,et al.  Effect of Gradation Segregation on Mechanical Properties of an Asphalt Mixture , 2019 .

[25]  Qinglin Guo,et al.  Stereological estimation of aggregate gradation using digital image of asphalt mixture , 2015 .

[26]  Ying Gao,et al.  Effects of flow rates and layer thicknesses for aggregate conveying process on the prediction accuracy of aggregate gradation by image segmentation based on machine vision , 2019, Construction and Building Materials.

[27]  Tao Xu,et al.  Investigation into causes of in-place rutting in asphalt pavement , 2012 .

[28]  Yanshun Jia,et al.  Quantitative analysis and visual presentation of segregation in asphalt mixture based on image processing and BIM , 2021 .

[30]  Yan Hong-guang.  Comprehensive evaluation method of semi-rigid base mixture's pavement performance , 2011 .

[31]  N. Hassan,et al.  Nondestructive Characterisation of the Effect of Asphalt Mixture Compaction on Aggregate Orientation and Segregation Using X-ray Computed Tomography , 2012 .

[32]  Duanyi Wang,et al.  Measurement of coarse aggregates movement characteristics within asphalt mixture using digital image processing methods , 2020 .

[33]  Morteza Vadood,et al.  Introducing a simple method to determine aggregate gradation of hot mix asphalt using image processing , 2014 .

[34]  Ehsan Jamshidi,et al.  Effect of Aggregate Gradation on Rutting of Asphalt Pavements , 2012 .

[35]  John T Harvey,et al.  Evaluation of Laboratory, Construction, and Performance Variability by Bootstrapping and Monte Carlo Methods for Rutting Performance Prediction of Heavy Vehicle Simulator Test Sections , 2011 .