Calibration and Application of Treatment Performance Models in a Pavement Management System in Tennessee
Publication: Journal of Transportation Engineering
Volume 141, Issue 2
Abstract
The usage of locally calibrated performance models and other related parameters is necessary in order to utilize the pavement management system (PMS) for maintenance strategy decision-making. In this study, the performance models of typical asphalt resurfacing treatments used in Tennessee were calibrated for the PMS by investigating historical maintenance projects. The multiple regression method was employed to analyze the influence of pretreatment PSI, traffic level, overlay thickness, and milling depth on the post-treatment performance curves. Specific designs of asphalt overlays and performance classes were determined according to the regression analysis. The performance curves for each identified treatment method at different performance classes were established and calibrated for the PMS. Rehabilitation trigger values, typical treatments and decision trees for pavement with different functional classes were determined and designed by investigating the historical maintenance projects. Utilizing the calibrated models, pavement maintenance optimization analyses were performed on a network level. The study has found that pavement with lower pretreatment roughness, thick overlay, and deep milling deteriorated at slower rate. Pavement with high traffic levels tended to have lower post-treatment roughness. The distress condition decreases much faster than smoothness. The most cost-effective network level maintenance strategy can be determined by conducting a network optimization analysis with a sufficient high budget limit. It is recommended that an optimized maintenance budget allocation should include combining cost-effectiveness, rolling unused budget over fiscal years, and re-distributing budget among different maintenance regions.
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Acknowledgments
The funding of this study was supported by the Tennessee Department of Transportation (TDOT). The TDOT pavement maintenance engineers are acknowledged for their assistance on field patching installation. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the TDOT, nor do the contents constitute a standard, specification, or regulation.
References
Albuquerque, F. S., and Núñez, W. P. (2011). “Development of roughness prediction models for low-volume road networks in northeast Brazil.”, Transportation Research Board, Washington, DC, 198–205.
de Solminihac, T. H., Hidalgo, S. P., and Salgado, T. M. (2003). “Calibration of performance models for surface treatment to Chilean conditions: The HDM-4 case.”, Transportation Research Board, Washington, DC, 285–293.
Dong, Q., and Huang, B. (2012). “Evaluation of influence factors on crack initiation of LTPP resurfaced-asphalt pavements using parametric survival analysis.” J. Perform. Constr. Facil., 412–421.
Dong, Q., Huang, B., Richards, S. H., and Yan, X. (2013a). “Cost-effectiveness analyses of maintenance treatments for low-and moderate-traffic asphalt pavements in Tennessee.” J. Transp. Eng., 797–803.
Dong, Q., Jiang, X., Huang, B., and Richards, S. H. (2013b). “Analyzing influence factors of transverse cracking on LTPP resurfaced asphalt pavements through NB and ZINB models.” J. Transp. Eng., 889–895.
Features of MicroPAVER. (2012). 〈http://www.cecer.army.mil/paver/Features.htm〉 (Oct. 16, 2012).
Garcia-Diaz, A., and Riggins, M. (1985). “Serviceability and distress methodology for predicting pavement performance.”, Transportation Research Board, Washington, DC, 56–61.
Haas, R., Hudson, W. R., and Zaniewski, J. (1994). Modern pavement management, Krieger Publishing Company, Malabar, FL.
Hong, F., and Chen, D. (2009). “Effects of surface preparation, thickness, and material on asphalt pavement overlay transverse crack propagation.” Can. J. Civ. Eng., 36(9), 1411–1420.
Jackson, N. M., and Reid, D. L. (2001). “Implementation of network level pavement management in Tennessee.” Fifth Int. Conf. on Managing Pavements, Univ. of Washington, Seattle, WA.
Li, J., Muench, S. T., Mahoney, J. P., Sivaneswaran, N., Pierce, L. M., and White, G. C. (2005). “The highway development and management system in Washington state: Calibration and application for the department of transportation road network.”, Transportation Research Board, Washington, DC, 53–61.
Lou, Z., Gunaratne, M., Lu, J. J., and Dietrich, B. (2001). “Application of neural network model to forecast short-term pavement crack condition: Florida case study.” J. Infrastruct. Syst., 166–171.
Prozzi, J. A., and Madanant, S. M. (2000). “Analysis of experimental pavement failure data using stochastic duration models.”, Transportation Research Board, Washington, DC, 87–94.
Rauhut, J. B., Von Quintus, H. L., and Eltahan, A. (2000). “Performance of rehabilitated asphalt concrete pavements in LTPP experiments (data collected through February 1997).”, Federal Highway Administration, U.S. Dept. of Transportation, Washington, DC.
Rohde, G. T., Jooste, F., Sadzik, E., and Henning, T. (1998). “The calibration and use of HDM-IV performance models in a pavement management system.” 4th Int. Conf. on Managing Pavements, CSIR Transportek, Pretoria, South Africa.
Rohde, G. T., Wolmarans, I., and Sadzik, E. (2002). “The calibration and validation of HDM performance models in the Gauteng PMS.” 21st Annual South African Transport Conf., Document Transformation Technologies, Irene, South Africa.
Sebaaly, P. E., Hand, A., Epps, J., and Bosch, C. (1996). “Nevada’s approach to pavement management.”, Transportation Research Board, Washington, DC, 109–117.
Tennessee Department of Transportation (TDOT). (2012). “Miles and vehicle miles of travel by functional class.” 〈http://www.tdot.state.tn.us/hpms/〉 (Oct. 16, 2012).
Yang, J., Gunaratne, M., Lu, J. J., and Dietrich, B. (2005). “Use of recurrent Markov chains for modeling the crack performance of flexible pavements.” J. Transp. Eng., 861–872.
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Published In
Journal of Transportation Engineering
Volume 141 • Issue 2 • February 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Sep 29, 2013
Accepted: Jul 28, 2014
Published online: Sep 4, 2014
Published in print: Feb 1, 2015
Discussion open until: Feb 4, 2015
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