Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures

Rutting has been considered the most serious distress in flexible pavements for many years. Flow number is an explanatory index for the evaluation of the rutting potential of asphalt mixtures. In this study, a promising variant of genetic programming, namely, gene expression programming (GEP), is utilized to predict the flow number of dense asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established on the basis of a series of uniaxial dynamic-creep tests conducted in this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple-least-squares-regression (MLSR) analysis was performed to benchmark the GEP models. For more verification, a subsequent parametric study was carried out, and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively capable of evaluating the flow number of asphalt mixtures. The GEP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers.

[1]  A Hofstra,et al.  PERMANENT DEFORMATION OF FLEXIBLE PAVEMENTS UNDER SIMULATED ROAD TRAFFIC CONDITIONS , 1972 .

[2]  W J Kenis,et al.  PREDICTIVE DESIGN PROCEDURES--A DESIGN METHOD FOR FLEXIBLE PAVEMENTS USING THE VESYS STRUCTURAL SUBSYSTEM , 1977 .

[3]  Harold L Von Quintus,et al.  COMPARATIVE EVALUATION OF LABORATORY COMPACTION DEVICES BASED ON THEIR ABILITY TO PRODUCE MIXTURES WITH ENGINEERING PROPERTIES SIMILAR TO THOSE PRODUCED IN THE FIELD , 1989 .

[4]  Robert P Elliott,et al.  EFFECT OF AGGREGATE GRADATION VARIATION ON ASPHALT CONCRETE MIX PROPERTIES , 1991 .

[5]  Roberto Todeschini,et al.  The data analysis handbook , 1994, Data handling in science and technology.

[6]  A T Visser,et al.  Calibration of HDM-III performance models for use in pavement management of South African national roads , 1995 .

[7]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[8]  Necati Kuloglu,et al.  Effect of Astragalus on Characteristics of Asphalt Concrete , 1999 .

[9]  S. Zoorob,et al.  Laboratory design and investigation of the properties of continuously graded Asphaltic concrete containing recycled plastics aggregate replacement (Plastiphalt) , 2000 .

[10]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[11]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[12]  Mohamed M El-Basyouny,et al.  SIMPLE PERFORMANCE TEST FOR SUPERPAVE MIX DESIGN , 2002 .

[13]  David H Timm,et al.  Calibration of Flexible Pavement Performance Equations for Minnesota Road Research Project , 2003 .

[14]  Simon Smith,et al.  Estimating key characteristics of the concrete delivery and placement process using linear regression analysis , 2003 .

[15]  S Hınıslıoglu,et al.  Use of waste high density polyethylene as bitumen modifier in asphalt concrete mix , 2004 .

[16]  Tom Scullion,et al.  VERIFICATION AND MODELING OF THREE-STAGE PERMANENT DEFORMATION BEHAVIOR OF ASPHALT MIXES , 2004 .

[17]  Serdal Terzi.  Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming , 2005 .

[18]  Ali Topal,et al.  Determination of fine aggregate angularity in relation with the resistance to rutting of hot-mix asphalt , 2005 .

[19]  Rafiqul A. Tarefder,et al.  Neural Network Model for Asphalt Concrete Permeability , 2005 .

[20]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[21]  Nelson Gibson.  A Viscoelastoplastic Continuum Damage Model for the Compressive Behavior of Asphalt Concrete , 2006 .

[22]  A. Aksoy,et al.  Investigation of rutting performance of asphalt mixtures containing polymer modifiers , 2007 .

[23]  Seyyed Soheil Sadat Hosseini,et al.  A Discussion on “Genetic programming for retrieving missing information in wave records along the west coast of India” [Applied Ocean Research 2007; 29 (3): 99–111] , 2008 .

[24]  P. Roy,et al.  On Some Aspects of Variable Selection for Partial Least Squares Regression Models , 2008 .

[25]  Ali Firat Cabalar,et al.  A genetic‐programming‐based formulation for the strength enhancement of fiber‐reinforced‐polymer‐confined concrete cylinders , 2008 .

[26]  Yong Pan,et al.  A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine. , 2009, Journal of hazardous materials.

[27]  Ali Firat Cabalar,et al.  Constitutive modeling of Leighton Buzzard Sands using genetic programming , 2010, Neural Computing and Applications.

[28]  C. Hsein Juang,et al.  Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks , 2009 .

[29]  M. M. Alinia,et al.  Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming , 2009 .

[30]  Halil Ceylan,et al.  Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt , 2009 .

[31]  Abdulkadir Çevik,et al.  Accumulated strain prediction of polypropylene modified marshall specimens in repeated creep test using artificial neural networks , 2009, Expert Syst. Appl..

[32]  Amir Hossein Gandomi,et al.  Multi expression programming: a new approach to formulation of soil classification , 2010, Engineering with Computers.

[33]  Andrei V. Lyamin,et al.  ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil , 2009 .

[34]  Amir Hossein Alavi,et al.  A robust data mining approach for formulation of geotechnical engineering systems , 2011 .