Abstract
Dynamic modulus |E*|, of Hot Mix Asphalt (HMA) is a crucial parameter in the pavement design and analysis. The Witczak regression-based model adopted by the Mechanistic-Empirical Pavement Design Guide (MEPDG) could be considered as the most fundamental and widely used model to estimate the dynamic modulus of HMA. However, the effect of confining stress has not been considered in this model as an effective parameter. In this paper, attempts were undertaken to develop a new predicting model for |E*| of HMA considering the effect of confining stress. Artificial Neural Networks (ANNs) was administrated as the computational tool for this modeling using 1320 |E*| test results performed at the University of Maryland. Asphalt mix parameters, test frequency, temperature as well as the level of the confining stress were considered as model inputs. Also, intercept of temperature susceptibility relationship (A) and slope of temperature susceptibility relationship (VTS) represented the effect of binder viscosity features. The new model could predict the |E*| of HMA with high accuracy of R2 = 0.99. Fundamentally, a comprehensive multiple-stage ANN-based sensitivity analysis was developed to survey whether the high accuracy is enough for a model to be considered perfect. Or despite the high accuracy, there may be other significant factors which are not considered as model inputs. Also, this sensitivity analysis shows which of the inputs has a fundamental role in the model. Moreover, for evaluating the level of influence of selected inputs, another sensitivity analysis was performed by the r-Pearson method and the results indicate that the confining stress has the highest increasing and temperature has the highest decreasing effect on |E*| with a significant difference with other inputs. The Pearson results confirm the ANN-based sensitivity analysis results.
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References
Witczak MW (2002) Simple performance test for superpave mix design. Transportation Research Board, Washington
Witczak M, Bari J (2004) Development of a master curve (E*) database for lime modified asphaltic mixtures, Arizona State University research report. Arizona State University, Temp
Witczak M, Fonseca O (1996) Revised predictive model for dynamic (complex) modulus of asphalt mixtures. Transp Res Rec 1540(1):15–23
AASHTO T 342-11 (2019) Standard method of test for determining dynamic modulus of hot-mix asphalt concrete mixtures. American Association of State and Highway Transportation Officials, Washington
Bonaquist RF, Christensen DW, Stump W (2003) Simple performance tester for Superpave mix design: first-article development and evaluation. Transportation Research Board, Washington
ARA I, ERES Consultants Division (2004) Guide for mechanistic—empirical design of new and rehabilitated pavement structures, final report. Part 3. Design analysis. Chapter 3, Design of new and reconstructed flexible pavements, prepared for NCHRP. Transportation Research Board, U.S. Department of Transportation
ARA I, ERES Consultants Division (2004) Guide for mechanistic—empirical design of new and rehabilitated pavement structures, final report, appendix GG-1: calibration of permanent deformation models for flexible pavements, prepared for NCHRP. Transportation Research Board, U.S. Department of Transportation
ARA I, ERES Consultants Division (2004) Appendix II-1, guide for mechanistic—empirical design of new and rehabilitated pavement structures, final report, appendix II-1: Calibration of fatigue cracking models for flexible pavements, prepared for NCHRP. Transportation Research Board, U.S. Department of Transportation
Shook J, Kallas B, McLeod N, Finn F, Pell P, Krchma L, Haas R, Anderson K (1969) Factors influencing dynamic modulus of asphalt concrete. In: Association of asphalt paving technologists proceedings
Asphalt Institute (1981) Thickness design-Asphalt pavements for air carrier airports. Asphalt Institute, College Park
Miller JS, Uzan J, Witczak MW (1983) Modification of the asphalt institute bituminous mix modulus predictive equation (Discussion)
Akhter GF, Witczak MW (1985) Sensitivity of flexible pavement performance to bituminous mix properties. Transp Res Rec 1034:70–79
Andrei D, Witczak M, Mirza M (1999) Development of a revised predictive model for the dynamic (complex) modulus of asphalt mixtures. In: Development of the 2002 guide for the design of new and rehabilitated pavement structures, NCHRP.
Bari J Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. Arizona State University Tempe, AZ2006
Brown SF, Darter M, Larson G, Witczak M, El-Basyouny MM (2006) Independent review of the" mechanistic-empirical pavement design guide" and software, NCHRP research results digest (307)
Ceylan H, Schwartz CW, Kim S, Gopalakrishnan K (2009) Accuracy of predictive models for dynamic modulus of hot-mix asphalt. J Mater Civ Eng 21(6):286–293
SakhaeiFar MS, Underwood BS, Ranjithan SR, Kim YR, Jackson N (2009) Application of artificial neural networks for estimating dynamic modulus of asphalt concrete. Transp Res Rec 2127(1):173–186
Gopalakrishnan K, Kim S (2011) Support vector machines approach to HMA stiffness prediction. ASCE J Eng Mech 137(2):138–146
Hashemi Jokar M, Khosravi A, Heidaripanah A, Soltani F (2018) Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system. Soft Comput 19:1–48
Sadrossadat E, Heidaripanah A, Osouli S (2016) Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems. Constr Build Mater 123:235–247
Heidaripanah A, Nazemi M, Soltani F (2016) Prediction of resilient modulus of lime-treated subgrade soil using different kernels of support vector machine. Int J Geomech 17(2):06016020
Nazemi M, Heidaripanah A (2016) Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials. Road Mater Pavement Des 17(3):768–778
Seitllari A, Kumbargeri YS, Biligiri KP, Boz I (2019) A soft computing approach to predict and evaluate asphalt mixture aging characteristics using asphaltene as a performance indicator. Mater Struct 52(5):100
Saffarzadeh M, Heidaripanah A (2009) Effect of asphalt content on the marshall stability of asphalt concrete using artificial neural networks. Int J Sci Technol Sci Iran 16(1):98–105
Duan Z, Poon CS, Xiao J (2017) Using artificial neural networks to assess the applicability of recycled aggregate classification by different specifications. Mater Struct 50(2):107
Elsanadedy H, Abbas H, Al-Salloum Y, Almusallam T (2016) Shear strength prediction of HSC slender beams without web reinforcement. Mater Struct 49(9):3749–3772
Güneyisi EM, Gesoğlu M, Güneyisi E, Mermerdaş K (2016) Assessment of shear capacity of adhesive anchors for structures using neural network based model. Mater Struct 49(3):1065–1077
Inthata S, Kowtanapanich W, Cheerarot R (2013) Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks. Mater struct 46(10):1707–1721
Miradi M, Molenaar A, Van de Ven M (2009) Performance modelling of porous asphalt concrete using artificial intelligence. Road Mater Pavement Des 10(sup1):263–280
Raab C, Halim AOAE, Partl MN (2013) Utilisation of artificial neural network for the analysis of interlayer shear properties. Baltic J Road Bridge Eng BJRBE 8(2):107–116
Haykin S (2010) Neural networks and learning machines. Pearson Education India, New Delhi
Hagan MT, Demuth HB (2014) Neural network design, 2nd edn. PWS Publishing Co., Mark Beale MHB Inc, Indianapolis
Pellinen TK, Witczak MW (2002) Use of stiffness of hot-mix asphalt as a simple performance test. Transp Res Rec 1789(1):80–90
Iman RL, Helton JC (1985) Comparison of uncertainty and sensitivity analysis techniques for computer models. Sandia National Labs, Albuquerque
Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Wiley, Amsterdam
Acknowledgements
This is to deeply appreciate UK National Health Service (NHS) for their effort, oncologists Dr. Rob Goldstein (Royal Free Hospital), Dr. Gholamreza Ehtejab (consultant), and Mr. Iype Satheesh the Surgeon (Royal Free Hospital) for the full support during my cancer medical treatment. The authors would also appreciate all of the creativities and great efforts of scientists which invented and advanced the Artificial Neural Networks concept including Warren McCulloch, Walter Pitts, Frank Rosenblatt, Alexey Ivakhnenko, Grigor’evich Lapa, Marvin Lee Minsky, Seymour Aubrey Papert, Donald Hebb, Bernard Widrow, Paul John Werbos, Henry J. Kelley, Arthur Earl Bryson, Seppo Ilmari Linnainmaa, Marcian Hoff , Teuvo Kohonen, Britt Anderson, John Hopfield, David Rumelhart and Jams McClelland and all other innovators in this area.
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Heidaripanah, A., Hassani, A. A fundamental multiple-stage ANN-based sensitivity analysis to predict the dynamic modulus of hot mix asphalt considering the effect of confining stress. Mater Struct 54, 15 (2021). https://doi.org/10.1617/s11527-020-01581-x
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DOI: https://doi.org/10.1617/s11527-020-01581-x