Monitoring the structural capacity of airfield pavement with built-in sensors and modulus back-calculation algorithm

Abstract The degree of deterioration in the structural capacity of a pavement is an important indicator of its performance. Conventional falling weight deflectometer, which assesses pavement capacity based on the deflection bowl data under impact loads and is the prevalent method in practice, only characterize the overall bearing capacity of pavement. It does not possess sufficient accuracy in measuring the modulus of pavement structural layer. In this paper, a smart pavement schema was proposed where built-in sensors are incorporated to monitor pavement stress and strain responses under aircraft loads. Theoretical relationship between pavement mechanical responses of a two-layered elastic system subjected to service load is established, which is used for back calculating the modulus of asphalt layer. To demonstrate the concept, sensors are deployed along a taxiway in Beijing Capital International Airport, Beijing, China. The measured mechanical responses by the sensors were incorporated to back-calculate the modulus of asphalt layer, which are then verified through dynamic modulus experiments. The results show that back-calculated modulus by incorporating sensor data is repeatable and can be applied for real-time evaluation of pavement performance without affecting the traffic. Extension of the model and analysis to multi-layered elastic system are discussed.

[1]  Nenad Gucunski,et al.  Comparative Study of Static and Dynamic Falling Weight Deflectometer Back-Calculations Using Probabilistic Approach , 2010 .

[2]  Sigurdur Erlingsson,et al.  Investigation of a pavement structural behaviour during spring thaw using falling weight deflectometer , 2013 .

[3]  Yanqing Zhao,et al.  Dynamic backcalculation of asphalt pavement layer properties using spectral element method , 2015 .

[4]  R. Roque,et al.  Evaluation of FWD Data for Determination of Layer Moduli of Pavements , 2003 .

[5]  R. Al-Khoury,et al.  Spectral element technique for efficient parameter identification of layered media; Part II; Inverse calculation , 2001 .

[6]  Dong Wang,et al.  Monitoring the Speed, Configurations, and Weight of Vehicles Using an In-Situ Wireless Sensing Network , 2015, IEEE Transactions on Intelligent Transportation Systems.

[7]  H. Lee,et al.  Viscowave – a new solution for viscoelastic wave propagation of layered structures subjected to an impact load , 2014 .

[8]  R. Al-Khoury,et al.  Spectral element technique for efficient parameter identification of layered media. I. Forward calculation , 2001 .

[9]  Dong Wang,et al.  A Prototype Integrated Monitoring System for Pavement and Traffic Based on an Embedded Sensing Network , 2015, IEEE Transactions on Intelligent Transportation Systems.

[10]  Mehmet Saltan,et al.  Fuzzy logic modeling of deflection behavior against dynamic loading in flexible pavements , 2007 .

[11]  Jaeyeon Cho,et al.  Estimation of in situ dynamic modulus by using MEPDG dynamic modulus and FWD data at different temperatures , 2013 .

[12]  Zejiao Dong,et al.  Analytical solutions of asphalt pavement responses under moving loads with arbitrary non-uniform tire contact pressure and irregular tire imprint , 2018 .

[13]  Ernian Pan,et al.  Inverse calculation of elastic moduli in cross-anisotropic and layered pavements by system identification method , 2015 .

[14]  Imad L. Al-Qadi,et al.  The Virginia smart road: The impact of pavement instrumentation on understanding pavement performance , 2004 .

[15]  Imad L. Al-Qadi,et al.  Data Collection and Management of the Instrumented Smart Road Flexible Pavement Sections , 2001 .

[16]  Maoyun Li,et al.  Comparative Study of Asphalt Pavement Responses under FWD and Moving Vehicular Loading , 2016 .

[17]  T. Garbowski,et al.  Multi-level backcalculation algorithm for robust determination of pavement layers parameters , 2017 .

[18]  Maoyun Li,et al.  Finite element modeling and parametric analysis of viscoelastic and nonlinear pavement responses under dynamic FWD loading , 2017 .

[19]  E. Pan,et al.  Backcalculation of pavement layer elastic modulus and thickness with measurement errors , 2014 .

[20]  R. Al-Khoury,et al.  Spectral element technique for efficient parameter identification of layered media. Part III: viscoelastic aspects , 2002 .

[21]  Fujian Ni,et al.  Dynamic response of pavement under FWD using spectral element method , 2014 .

[22]  Raj Bridgelall,et al.  Road roughness evaluation using in-pavement strain sensors , 2015 .

[23]  Clara Celauro,et al.  Backcalculation of airport pavement moduli and thickness using the Lévy Ant Colony Optimization Algorithm , 2016 .

[24]  Mehmet Saltan,et al.  Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement , 2012, Neural Computing and Applications.

[25]  Sudhir Varma,et al.  Backcalculation of viscoelastic and nonlinear flexible pavement layer properties from falling weight deflections , 2016 .