Calculation of Equivalent Axle Load Factor Based on Artificial Intelligence

Document Type : Regular Paper

Authors

1 Ph.D. Candidate, Department of Civil Engineering, Payame Noor University, P.O.Box 19395-4697, Tehran, Iran

2 Associate Professor, Department of Civil Engineering, Payame Noor University, P.O.Box 19395-4697, Tehran, Iran

3 Assistant Professor, Faculty of Engineering, Ferdowsi University, P.O.Box 9177948974, Mashhad, Iran

Abstract

In most road pavements design methods, a solution is required to transform the traffic spectrum to standard axle load with using equivalent axle load factor (EALF). The EALF depends on various parameters, but in existing design methods, only the axle type (single, tandem, and tridem) and pavement structure number were considered. Also, the EALF only determined for experimental axles and axle details (i.e., axle weight, length, pressure), wheel type (single or dual wheel) plus pavement properties were overlooked which may cause inaccuracy and unusable for the new axle. This paper presented a developed model based on Artificial Neural Network (ANN) for calculation of EALFs considering axle type, axle length, contact area, pavement structure number (SN), tire pressure, speed, and final serviceability. Backpropagation architecture was selected for the model for the EALF prediction based on fatigue criteria. Finally, among all reviewed ANN configuration, a network with 7-13-1 was selected for the optimum network.

Keywords

Main Subjects

References

[1]      Zaghloul S, White TD. Guidelines for Permitting Overloads; Part 1: Effect of Overloaded Vehicles on the Indiana Highway Network. West Lafayette, IN: 1994.
[2]      Chatti K, Lee D, Kim T. Truck Damage Factors Using Dissipated Energy versus Peak Strains. 6th Int. Symp. Heavy Veh. Weight. Dlmensiolns, Saskatoon: 2000, p. 175–83.
[3]      Abdel-Motaleb ME. Impact of high-pressure truck tires on pavement design in Egypt. Emirates J Eng Res 2007;12:65–73.
[4]      Judycki J. Determination of Equivalent Axle Load Factors on the Basis of Fatigue Criteria for Flexible and Semi-Rigid Pavements. Road Mater Pavement Des 2010;11:187–202.
[5]      Chaudry R, Memon AB. Effects of Variation in Truck Factor on Pavement Performance in Pakistan 2013;32:19–30.
[6]      Amorim SIR, Pais JC, Vale AC, Minhoto MJC. A model for equivalent axle load factors. Int J Pavement Eng 2014;16:881–93.
[7]      Rys D, Judycki J, Jaskula P. Determination of Vehicles Load Equivalency Factors for Polish Catalogue of Typical Flexible and Semi-rigid Pavement Structures. Transp Res Procedia 2016;14:2382–91.
[8]      Zhang H, Gong M, Yu T. Modification and application of axle load conversion formula to determine traffic volume in pavement design. Int J Pavement Res Technol 2018;11:582–93.
[9]      Singh AK, Sahoo JP. Analysis and design of two layered flexible pavement systems: A new mechanistic approach. Comput Geotech 2020;117:103238.
[10]     Deng Y, Luo X, Zhang Y, Lytton RL. Evaluation of flexible pavement deterioration conditions using deflection profiles under moving loads. Transp Geotech 2021;26:100434.
[11]     Rezazadeh Eidgahee D, Rafiean AH, Haddad A. A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iran J Sci Technol Trans Civ Eng 2020;44:219–29.
[12]     Rezazadeh Eidgahee D, Fasihi F, Naderpour H. Optimized Artificial Neural Network for Analyzing Soil-Waste Rubber Shred Mixtures. Sharif J Civ Eng 2015;31.2:105–11.
[13]     Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran 2019;26:3233–44.
[14]     Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16.
[15]     Azunna SU, Nwafor EO, Ojobo SO. Stabilization of Ikpayongu laterite using Cement, RHA and Carbide Waste Mixture for Road Subbase and Base Material. Comput Eng Phys Model 2020;3:77–96.
[16]     Al-Qadi IL, Wang H, Tutumluer E. Dynamic Analysis of Thin Asphalt Pavements by Using Cross-Anisotropic Stress-Dependent Properties for Granular Layer. Transp Res Rec J Transp Res Board 2010;2154:156–63.
[17]     Chen Y. Viscoelastic modeling of flexible pavement. Akron, 2009.
[18]     Alkaissi ZA. Effect of high temperature and traffic loading on rutting performance of flexible pavement. J King Saud Univ - Eng Sci 2020;32:1–4.
[19]     Zaghloul SM, White T. Use of a three-dimensional, dynamic finite element program for analysis of flexible pavement. Transp Res Rec 1993.
[20]     Zarei B, Shafabakhsh GA. Dynamic Analysis of Composite Pavement using Finite Element Method and Prediction of Fatigue Life 2018;04:33–7.
[21]     Huang YH. Pavement Analysis and Design. Second Edi. Pearson Education; 2004.
[22]     Kim M. Three-Dimensional Finite Element Analysis Of Flexible Pavements Considering Nonlinear Pavement Foundation Behavior. University of Illinois, 2007.
[23]     Cebon D. Handbook of vehicle-road interaction. 1999.
[24]     Boulos Filho P, Raymundo H, Machado ST, Leite ARCAP, Sacomano JB. CONFIGURATIONS OF TIRE PRESSURE ON THE PAVEMENT FOR COMMERCIAL VEHICLES: CALCULATION OF THE ‘N’ NUMBER AND THE CONSEQUENCES ON PAVEMENT PERFORMANCE. Indep J Manag Prod 2016;7:584–605.
[25]     Filho PB, Raymundo H, Machado ST, Leite ARCAP, Sacomano JB. Configurations of tire pressure on the pavement for commercial vehicles: calculation of the n number and the consequences on pavement performance. Indep J Manag Prod 2016;7:584–605.
[26]     Uddin W, Garza S. 3D-FE Modeling and Simulation of Airfield Pavements Subjected to FWD Impact Load Pulse and Wheel Loads. Airf. Pavements, Reston, VA: American Society of Civil Engineers; 2004, p. 304–15.
[27]     Sebaaly P, Tabatabaee N, Kulakowski B, Scullion T. Instrumentation for Flexible Pavements-Field Performance of Selected Sensors Volume I : Final Report. vol. I. 1992.
[28]     Solatifar N, Lavasani SM. Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data. J Rehabil Civ Eng 2020;8:121–32.
[29]     Abbaszadeh MA, Sharbatdar M. Modeling of Confined Circular Concrete Columns Wrapped by Fiber Reinforced Polymer Using Artificial Neural Network. J Soft Comput Civ Eng 2020;4:61–78.
[30]     LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.
[31]     Moradi E, Naderpour H, Kheyroddin A. An artificial neural network model for estimating the shear contribution of RC beams strengthened by externally bonded FRP. J Rehabil Civ Eng 2018;6:88–103.
[32]     Darvishan E. The Punching Shear Capacity Estimation of FRP-Strengthened RC Slabs Using Artificial Neural Network and Group Method of Data Handling. J Rehabil Civ Eng 2021;9:102–13.
[33]     Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84.
[34]     Adeli H. Neural Networks in Civil Engineering: 1989-2000. Comput Civ Infrastruct Eng 2001;16:126–42.
[35]     da Silva IN, Hernane Spatti D, Andrade Flauzino R, Liboni LHB, dos Reis Alves SF. Artificial Neural Network Architectures and Training Processes. Artif. Neural Networks, Cham: Springer International Publishing; 2017, p. 21–8.
[36]     Abdulla NA. Using the artificial neural network to predict the axial strength and strain of concrete-filled plastic tube. J Soft Comput Civ Eng 2020;4:63–86.
[37]     Gajewski J, Sadowski T. Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method. Comput Mater Sci 2014;82:114–7.
[38]     Ghasemi M, Roshani GH, Roshani A. Detecting Human Behavioral Pattern in Rock, Paper, Scissors Game Using Artificial Intelligence. Comput Eng Phys Model 2020;3:25–35.
[39]     Profillidis VA, Botzoris GN. Modeling of Transport Demand. Elsevier; 2018.
[40]     Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91.
[41]     Kanchidurai S, Krishnan PA, Baskar K. Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirical and Neural Network Models. J Soft Comput Civ Eng 2020;4:24–35.
[42]     Priyadarshee A, Chandra S, Gupta D, Kumar V. Neural Models for Unconfined Compressive Strength of Kaolin Clay Mixed with Pond Ash, Rice Husk Ash and Cement. J Soft Comput Civ Eng 2020;4:85–102.
[43]     Hagan MT, Demuth HB, Beale MH, De Jess O. Neural network design. 2nd ed. USA: Martin Hagan; 2014.
[44]     Jahangir H, Rezazadeh Eidgahee D. A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation. Compos Struct 2021;257:113160.
[45]     Kalman Sipos T, Parsa P. Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks. J Soft Comput Civ Eng 2020;4:111–26.
[46]     Angus JE. Criteria for choosing the best neural network. Sandiego: 1991.
[47]     Papadimitropoulos VC, Tsikas PK, Chassiakos AP. Modeling the Influence of Environmental Factors on Concrete Evaporation Rate. J Soft Comput Civ Eng 2020;4:79–97.

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History

  • Receive Date: 12 January 2021
  • Revise Date: 14 March 2021
  • Accept Date: 01 May 2021

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