A Computational Methodology for Assessing the Time‐Dependent Structural Performance of Electric Road Infrastructures

An infrastructure adapted to dynamic wireless recharging of electric vehicles is often referred to generically as Electric Road "e-road". E-roads are deemed to become essential components of future grid environments and smart city strategies. Several technologies already exist that propose different ways to integrate dynamic inductive charging systems within the infrastructure. One e-road solution uses a very thin rail with box-section made of fibre-reinforced polymer, inside which an electric current flows producing a magnetic field. In spite of the great interest and research generated by recharging technologies, the structural problems of e-roads, including vibrations and structural integrity in the short and/or long period, have received relatively little attention to date. This article presents a novel computational methodology for assessing the time-dependent structural performance of e-roads, including a recursive strategy for the estimation of the lifetime of surface layers. The article also reports some numerical findings about e-roads that will drive further numerical analyses and experimental studies on this novel type of infrastructure. Finally, numerical simulations have been conducted to compare an e-road with a traditional road "t-road", in terms of static, dynamic and fatigue behavior.

[1]  Sigurdur Erlingsson,et al.  Evaluation of permanent deformation models for unbound granular materials using accelerated pavement tests , 2013 .

[2]  Audrius Vaitkus,et al.  Monitoring the Mechanical and Structural Behavior of the Pavement Structure Using Electronic Sensors , 2015, Comput. Aided Civ. Infrastructure Eng..

[3]  T. Guena,et al.  How Depth of Discharge Affects the Cycle Life of Lithium-Metal-Polymer Batteries , 2006, INTELEC 06 - Twenty-Eighth International Telecommunications Energy Conference.

[4]  Dan M. Frangopol,et al.  Maintenance Principles for Civil Structures , 2009 .

[5]  S. Subramanyan,et al.  A Cumulative Damage Rule Based on the Knee Point of the S-N Curve , 1976 .

[6]  Shen-Haw Ju,et al.  Finite element investigation of traffic induced vibrations , 2009 .

[7]  A. Ahlbom.  Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz) , 1998 .

[8]  Loay Akram Al-Khateeb,et al.  Rutting Prediction of Flexible Pavements Using Finite Element Modeling , 2011 .

[9]  Yiqun Tang,et al.  A prediction method using grey model for cumulative plastic deformation under cyclic loads , 2012, Natural Hazards.

[10]  Roslan Abd. Rahman,et al.  Groundhook control of semi-active suspension for heavy vehicle , 2012 .

[11]  Devin K. Harris.  Lateral Load Distribution and Deck Design Recommendations for the Sandwich Plate System (SPS) in Bridge Applications , 2007 .

[12]  Hojjat Adeli,et al.  Life‐cycle cost optimization of steel structures , 2002 .

[13]  P Baburamani,et al.  ASPHALT FATIGUE LIFE PREDICTION MODELS: A LITERATURE REVIEW , 1999 .

[14]  Sigurdur Erlingsson,et al.  Predicting permanent deformation behaviour of unbound granular materials , 2015 .

[15]  M. Ben-Amoz.  Cumulative damage model based on two-mode fatigue damage bounds , 2009 .

[16]  Nils-Erik Wiberg,et al.  Simulation of inelastic deformation in road structures due to cyclic mechanical and thermal loads , 2007 .

[17]  J Litzka,et al.  Measurements of the lateral distribution of heavy vehicles and its effects on the design of road pavements , 1995 .

[18]  Niki D. Beskou,et al.  Dynamic effects of moving loads on road pavements: A review , 2011 .

[19]  Andrew Dawson,et al.  Calculating rutting of some thin flexible pavements from repeated load triaxial test data , 2015 .

[20]  Nicole Kringos,et al.  Electrification of roads: Opportunities and challenges , 2015 .

[21]  Tien Fang Fwa,et al.  Critical Rut Depth for Pavement Maintenance Based on Vehicle Skidding and Hydroplaning Consideration , 2012 .

[22]  I. S. Suh.  Application of Shaped Magnetic Field in Resonance (SMFIR) Technology to Future Urban Transportation , 2011 .

[23]  H. Adeli,et al.  Augmented Lagrangian genetic algorithm for structural optimization , 1994 .

[24]  Jin-Hoon Jeong,et al.  Testing and modelling of friction characteristics between concrete slab and subbase layers , 2014 .

[25]  K. Jokela,et al.  ICNIRP Guidelines GUIDELINES FOR LIMITING EXPOSURE TO TIME-VARYING , 1998 .

[26]  Dallas N. Little,et al.  A modified viscoplastic model to predict the permanent deformation of asphaltic materials under cyclic-compression loading at high temperatures , 2012 .

[27]  Hailin Yao,et al.  Experimental evaluation and theoretical analysis of multi-layered road cumulative deformation under dynamic loads , 2014 .

[28]  Hojjat Adeli,et al.  Recent advances in health monitoring of civil structures , 2014 .

[29]  Chengming Lan,et al.  Traffic load modelling based on structural health monitoring data , 2011 .

[30]  Youngguk Seo,et al.  Using Acoustic Emission to monitor fatigue damage and healing in Asphalt Concrete , 2008 .

[31]  C. Hsein Juang,et al.  Normalized Shear Modulus and Material Damping Ratio Relationships , 2005 .

[32]  Praveen Kumar,et al.  Critical review of flexible pavement performance models , 2014 .

[33]  Hojjat Adeli,et al.  Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures , 2008 .

[34]  A. Schroeder,et al.  The economics of fast charging infrastructure for electric vehicles , 2012 .

[35]  Ryan Fries,et al.  Infrastructure Cost Issues Related to Inductively Coupled Power Transfer for Electric Vehicles , 2014, ANT/SEIT.

[36]  Michael Kaliske,et al.  A continuum mechanical approach to model asphalt , 2015 .

[37]  Eyal Levenberg,et al.  Analysis of pavement response to subsurface deformations , 2013 .

[38]  Zion Tsz Ho Tse,et al.  Electric vehicle wireless charging technology: a state-of-the-art review of magnetic coupling systems , 2014 .

[39]  Samer W Katicha,et al.  Analysis of Hot Mix Asphalt (HMA) Linear Viscoelastic and Bimodular Properties Using Uniaxial Compression and Indirect Tension (IDT) Tests , 2007 .

[40]  Soohyok Im,et al.  Nonlinear viscoelastic approach to model damage-associated performance behavior of asphaltic mixture and pavement structure , 2013 .

[41]  Fujie Zhou,et al.  Development and Verification of the Overlay Tester Based Fatigue Cracking Prediction Approach , 2007 .

[42]  André-Gilles Dumont,et al.  Strain and stress distributions in flexible pavements under moving loads , 2004 .

[43]  Sungho Mun,et al.  Development of a remaining fatigue life model for asphalt black base through accelerated pavement testing , 2008 .

[44]  Reilly Jp.  Comments concerning "Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz)". , 1999 .

[45]  Chuntaek Rim,et al.  The development and deployment of On-Line Electric Vehicles (OLEV) , 2013 .

[46]  Donato Ciampa,et al.  Correction to: The vibrations induced by surface irregularities in road pavements – a Matlab® approach , 2019, European Transport Research Review.

[47]  Ghassan R. Chehab,et al.  Determination of Time-domain Viscoelastic Functions using Optimized Interconversion Techniques , 2007 .

[48]  Geert Lombaert,et al.  The effect of road unevenness on the dynamic vehicle response and ground-borne vibrations due to road traffic , 2011 .

[49]  Sigurdur Erlingsson,et al.  Modelling of responses and rutting profile of a flexible pavement structure in a heavy vehicle simulator test , 2015 .

[50]  Grant Anthony Covic,et al.  Modern Trends in Inductive Power Transfer for Transportation Applications , 2013, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[51]  Xiaoming Sun,et al.  A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory , 2014, Comput. Aided Civ. Infrastructure Eng..

[52]  Joel P. Conte,et al.  Elements of an integrated health monitoring framework , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[53]  Jorge C. Pais,et al.  The prediction of fatigue life using the k1-k2 relationship , 2009 .

[54]  R. Brook,et al.  Cumulative Damage in Fatigue: A Step towards Its Understanding , 1969 .