In-Time Density Monitoring of In-Place Asphalt Layer Construction via Intelligent Compaction Technology
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 1
Abstract
Intelligent compaction (IC) has been successfully used for soil and base compaction of highways. However, the application of IC technology to monitor the construction quality of asphalt pavement faces complications with compaction processing. This study monitored the compaction process of asphalt layers using an IC-based method. The compaction data were first collected during the construction of a local road in Mardan, Pakistan, including IC data, in-place density, and temperature at the asphalt layer surface. The collected IC data were then used to compute the intelligent compaction measurement values (ICMVs). The support vector regression analysis was performed to predict the roller amplitude and in-place density using the ICMVs. To explore the correlations in compaction measuring/monitoring indicators, this study also explored the correlations between the ICMVs with core density, temperature, and amplitude. Experiment results indicated that the predicted roller amplitude values from the support vector regression model were close to the measured ones. There were high correlations between the roller amplitudes and temperatures with the compaction measurement values (CMVs). In contrast, the correlation between the in-place core densities and CMV values was low. Additionally, the CMVs of the backward pass were higher than the forward one in each compaction cycle because the pavement density increased and the air void decreased after each forward pass.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the National Key Research and Development Project (Grant No. 2020YFB1600102), the National Key Research and Development Project (Grant No. 2020YFA0714302), the National Natural Science Foundation of China (Grant No. 5210081588), and Intelligent monitoring of airport pavement status and rapid performance recovery technology, which are highly acknowledged.
References
Asif Imran, S., M. Barman, S. Commuri, M. Zaman, and M. Nazari. 2018. “Artificial neural network–based intelligent compaction analyzer for real-time estimation of subgrade quality.” Int. J. Geomech. 18 (6): 04018048. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001089.
Beainy, F., S. Commuri, and M. Zaman. 2010. “Asphalt compaction quality control using artificial neural network.” In Proc., 49th IEEE Conf. on Decision and Control. New York: IEEE.
Chang, G., Q. Xu, J. Rutledge, B. Horan, L. Michael, D. White, and P. Vennapusa. 2011. Accelerated implementation of intelligent compaction technology for embankment subgrade soils, aggregate base, and asphalt pavement materials. Washington, DC: Federal Highway Administration.
Chang, G. K., Q. Xu, J. L. Rutledge, and S. I. Garber. 2014. A study on intelligent compacting and in-place asphalt density. Washington, DC: Federal Highway Administration.
Cheng, M.-Y., D. Prayogo, and Y.-W. Wu. 2019. “Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression.” Neural Comput. Appl. 31 (10): 6261–6273. https://doi.org/10.1007/s00521-018-3426-0.
Commuri, S., A. T. Mai, and M. Zaman. 2011. “Neural network—Based intelligent compaction analyzer for estimating compaction quality of hot asphalt mixes.” J. Constr. Eng. Manage. 137 (9): 634–644. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000343.
Forssblad, L. 1980. “Compaction meter on vibrating rollers for improved compaction control.” In Proc., Int. Conf. on Compaction. Paris: Laboratoire Central des Ponts et Chausees.
Hadjidemetriou, G. M., P. A. Vela, and S. E. Christodoulou. 2018. “Automated pavement patch detection and quantification using support vector machines.” J. Comput. Civ. Eng. 32 (1): 04017073. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000724.
Hu, J., T. Ma, Y. Zhu, X. Huang, and J. Xu. 2021. “A feasibility study exploring limestone in porous asphalt concrete: Performance evaluation and superpave compaction characteristics.” Constr. Build. Mater. 279 (21): 122457. https://doi.org/10.1016/j.conbuildmat.2021.122457.
Hu, W., B. Huang, X. Shu, and M. Woods. 2017. “Utilising intelligent compaction meter values to evaluate construction quality of asphalt pavement layers.” Road Mater. Pavement Des. 18 (4): 980–991. https://doi.org/10.1080/14680629.2016.1194882.
Hu, W., X. Jia, X. Zhu, H. Gong, G. Xue, and B. Huang. 2019. “Investigating key factors of intelligent compaction for asphalt paving: A comparative case study.” Constr. Build. Mater. 229 (11): 116876. https://doi.org/10.1016/j.conbuildmat.2019.116876.
Ma, Y., F. Chen, T. Ma, X. Huang, and Y. Zhang. 2021. Intelligent compaction: An improved quality monitoring and control of asphalt pavement construction technology. New York: IEEE.
Ma, Y., Y. Zhang, W. Zhao, X. Ding, Z. Wang, and T. Ma. 2022. “Assessment of intelligent compaction quality evaluation index and uniformity.” J. Transp. Eng. Part B: Pavements 148 (2): 04022024. https://doi.org/10.1061/JPEODX.0000368.
Maalouf, M., N. Khoury, and T. B. Trafalis. 2008. “Support vector regression to predict asphalt mix performance.” Int. J. Numer. Anal. Methods Geomech. 32 (16): 1989–1996. https://doi.org/10.1002/nag.718.
Mooney, M., and D. Adam. 2007. “Vibratory roller integrated measurement of earthwork compaction: An overview.” In Proc., 7th FMGM 2007: Field Measurements in Geomechanics, 1–12. Reston, VA: ASCE.
Mooney, M. A. 2010. Intelligent soil compaction systems. Washington, DC: Transportation Research Board.
Mooney, M. A., and R. V. Rinehart. 2007. “Field monitoring of roller vibration during compaction of subgrade soil.” J. Geotech. Geoenviron. Eng. 133 (3): 257–265. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:3(257).
Pettersson, C., and Å. Sandström. 2004. “Intelligent systems for QA/QC in soil compaction.” In Proc., 83rd Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Rader, C., and N. Brenner. 1976. “A new principle for fast Fourier transformation.” IEEE Trans. Acoust. Speech Signal Process. 24 (3): 264–266. https://doi.org/10.1109/TASSP.1976.1162805.
Sandström, Å. 1994. Numerical simulation of a vibratory roller on cohesionless soil. Stockholm, Sweden: Geodynamic.
Savan, C. M., K. W. Ng, and K. Ksaibati. 2015. Implementation of intelligent compaction technologies for road constructions in Wyoming. Washington, DC: Mountain Plains Consortium.
Scherocman, J. A., S. Rakowski, and K. Uchiyama. 2007. “Intelligent compaction, does it exist?” In Proc., 52nd Annual Conf. of the Canadian Technical Asphalt Association. West Kelowna, BC, Canada: Canadian Technical Asphalt Association.
Schölkopf, B., A. J. Smola, and F. Bach. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
Seraj, F., B. J. Zwaag, A. Dilo, T. Luarasi, and P. Havinga. 2015. “RoADS: A road pavement monitoring system for anomaly detection using smart phones.” In Big data analytics in the social and ubiquitous context, 128–146. Berlin: Springer.
Shannon, C. E. 1949. “Communication in the presence of noise.” Proc. IRE 37 (1): 10–21.
Thurner, H. 1980. “A new device for compaction control.” In Proc., Int. Conf. on Compaction. Berlin: VDM Verlag Dr. Mueller e.K.
White, D. J., P. Vennapusa, and M. J. Thompson. 2007. Field validation of intelligent compaction monitoring technology for unbound materials. St. Paul, MN: Minnesota DOT.
Xu, Q., and G. K. Chang. 2013. “Evaluation of intelligent compaction for asphalt materials.” Autom. Constr. 30 (56): 104–112. https://doi.org/10.1016/j.autcon.2012.11.015.
Yuan, M., F. Zhou, and H. Tao. 2021. “Dynamic simulation and evolution of key control parameters for intelligent compaction of subgrade.” J. Central South Univ. Sci. Technol. 52 (7): 2246–2257.
Zhang, W., A. Khan, J. Huyan, J. Zhong, T. Peng, and H. Cheng. 2021a. “Predicting Marshall parameters of flexible pavement using support vector machine and genetic programming.” Constr. Build. Mater. 306 (12): 124924. https://doi.org/10.1016/j.conbuildmat.2021.124924.
Zhang, W., A. R. Khan, S. Yoon, J. Lee, R. Zhang, and K. Zeng. 2021b. “Investigation of the correlations between the field pavement in-place density and the intelligent compaction measure value (ICMV) of asphalt layers.” Constr. Build. Mater. 292 (Dec): 123439. https://doi.org/10.1016/j.conbuildmat.2021.123439.
Ziari, H., M. Maghrebi, J. Ayoubinejad, and S. T. Waller. 2016. “Prediction of pavement performance: Application of support vector regression with different kernels.” Transp. Res. Rec. 2589 (1): 135–145. https://doi.org/10.3141/2589-15.
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Published In
Journal of Materials in Civil Engineering
Volume 35 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 30, 2021
Accepted: May 6, 2022
Published online: Oct 31, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 31, 2023
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