Pavement Health Monitoring System Based on an Embedded Sensing Network
Publication: Journal of Materials in Civil Engineering
Volume 26, Issue 10
Abstract
As one of the most important and expensive investments/assets in modern society, asphalt concrete pavements age and deteriorate with time as a result of asphalt mixture aging, cumulative loading, environmental conditions, and/or inadequate maintenance. The detection of pavement health condition is very important for a pavement analysis and management system. In this paper, a pavement health monitoring system was developed based on an embedded sensing network with an efficient combination of various commercial pavement sensors. The collected pavement responses were clear and reasonable and compared with a numerical simulation. The modulus of the pavement surface layer can be back-calculated every year or every several months based on testing runs and numerical models. Fatigue cracking and rutting models were selected to predict the distress of the experimental section according to the actual strain measurement from each passing vehicle. A health monitoring system, which contains the continuous monitoring and periodic testing as routines, is proposed as an important component of a pavement management system.
Get full access to this article
View all available purchase options and get full access to this article.
References
ABAQUS version 6.12 [Computer software]. Providence, RI, Dassault Systèmes Simulia.
Al-Qadi, I. L., Loulizi, A., Elseifi, M., and Lahouar, S. (2004). “The Virginia smart road: The impact of pavement instrumentation on understanding pavement performance.” J. Assoc. Asphalt Paving Technol., 73, 427–465.
Corley-Lay, J., Jadoun, F., Mastin, J., and Kim, Y. (2010). “Comparison of flexible pavement distresses monitored by North Carolina department of transportation and long-term pavement performance program.” J. Transp. Res. Board, 2153, 91–96.
CTLGroup. (2013). “Asphalt strain gages.” 〈http://assets.ctlgroup.com/a10a2278-a248-46b1-a0cc-5ce4ec91199b.pdf〉 (Nov. 26, 2013).
Goel, A., and Das, A. (2008). “Nondestructive testing of asphalt pavements for structural condition evaluation: A state of the art.” Nondestr. Test. Eval., 23(2), 121–140.
Huang, Y. H. (2004). Pavement analysis and design, 2nd Ed., Prentice Hall, Upper Saddle River, NJ.
Huff, R., Berthelot, C., and Daku, B. (2005). “Continuous primary dynamic pavement response system using piezoelectric axle sensors.” Can. J. Civ. Eng., 32(1), 260–269.
Koutsopoulos, H., and Downey, A. (1993). “Primitive based classification of pavement cracking images.” J. Transp. Eng., 402–418.
Lukanen, E. O. (2005). “Load testing of instrumented pavement sections.” Minnesota Dept. of Transportation, Research Services Section, St. Paul, MN.
MATLAB R2013a [Computer software]. Natick, MA, MathWorks.
National Cooperative Highway Research Program (NCHRP). (2011). Mechanistic-empirical pavement design guide, NCHRP, Transportation Research Board, National Research Council, Washington, DC.
Mills, J. P., Newton, I., and Peirson, G. C. (2001). “Pavement deformation monitoring in a rolling load facility.” Photogramm. Rec., 17(97), 7–24.
Mohajeri, M. H., and Manning, P. J. (1991). “ARIA: An operating system of pavement distress diagnosis by image processing.” Transportation Research Record 1311, Transportation Research Board, Washington, DC, 120–130.
Potter, J. F., Mayhew, H. C., and Mayo, A. P. (1969). Instrumentation of the full scale experiment on A1 trunk road at Conington, Huntingdonshire, Transport Research Laboratory (Road Research Laboratory), Wokingham, Berkshire, U.K.
Rollings, R. S., and Pittman, D. W. (1992). “Field instrumentation and performance monitoring of rigid pavements.” J. Transp. Eng., 361–370.
Sebaaly, P. E., Tabatabaee, N., and Kulakowski, B. (1995). “Evaluation of the Hall-effect sensor for pavement instrumentation.” J. Test. Eval., 23(3), 189–195.
Sebaaly, P. E., Tabatabaee, N., Kulakowski, B., and Scullion, T. (1991). “Instrumentation for flexible pavements—Field performance of selected sensors.” Federal Highway Administration, Washington, DC.
Signore, J. M., and Roesler, J. R. (1995). “Using fiber-optic sensing techniques to monitor behavior of transportation materials.” Transportation Research Record 1478, Transportation Research Board, Washington, DC, 37–43.
Timm, D. H., and Priest, A. L. (2004). “Dynamic pavement response data collection and processing at the NCAT test track.”, National Center for Asphalt Technology, Auburn Univ, Auburn, AL.
Timm, D. H., Priest, A. L., and McEwen, T. V. (2004). “Design and instrumentation of the structural pavement experiment at the NCAT test track.” National Center for Asphalt Technology, Auburn, AL.
Tokyo Sokki Kenkyujo. (2013). “KDE-PA/KDF-PA soil pressure gauge.” 〈http://www.tml.jp/e/product/transducers/civil_eng/soil_pressure/kde_kdf.html〉 (Nov. 26, 2013).
Xue, W. J., and Weaver, E. (2011). “Pavement shear strain response to dual and wide-base tires.” J. Transp. Res. Board, 2225, 155–164.
You, Z. P., Goh, S. W., and Dong, J. P. (2012). “Predictive models for dynamic modulus using weighted least square nonlinear multiple regression model.” Can. J. Civ. Eng., 39(5), 589–597.
Zhou, Z., et al. (2012). “Optical fiber Bragg grating sensor assembly for 3D strain monitoring and its case study in highway pavement.” Mech. Syst. Signal Process., 28, 36–49.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 26 • Issue 10 • October 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jun 28, 2013
Accepted: Nov 4, 2013
Published online: Nov 6, 2013
Published in print: Oct 1, 2014
Discussion open until: Oct 22, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.