Alternative Approach for Predicting the Phase Angle Characteristics of Asphalt Concrete Mixtures Based on Recurrent Neural Networks
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 9
Abstract
Laboratory performance testing of the phase angle of asphalt concrete (AC) mixtures is often expensive and requires enormous human effort and time. To circumvent this problem, several regression-based methods have been proposed in the literature to model the phase angle behavior of AC mixtures using various approaches. However, these methods impose strict assumptions on the underlying relationship between phase angle and its corresponding covariates as well as how well and accurately these covariates are measured, restricting us from fully analyzing the predictive capability of any modeling method. To this end, this study proposed an alternative approach for modeling the phase angle characteristics of AC mixtures based on a recurrent neural network (RNN) that inherently and implicitly captures the effects of covariates. This approach is suitable to model the sequential nature of data recorded in laboratory testing where phase angle testing was repeated for a set of six loading frequencies forming a recurrent pattern. The proposed RNN model (P-RNN) was applied separately to wearing and base course mixtures by considering the historical values of phase angle as input and to predict its value for the next loading frequency, keeping temperature as a constant. To demonstrate the superiority of the proposed approach, the P-RNN model is compared with other competing models from the literature, and the results reveal superior performance of the P-RNN model.
Get full access to this article
View all available purchase options and get full access to this article.
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 did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
References
AASHTO. 2007. TP 62-07 Standard test method for determining the dynamic modulus of hot mix asphalt (HMA). Washington, DC: AASHTO.
Abdić, I., L. Fridman, D. E. Brown, W. Angell, B. Reimer, E. Marchi, and B. Schuller. 2016. “Detecting road surface wetness from audio: A deep learning approach.” In Proc., 2016 23rd Int. Conf. on Pattern Recognition (ICPR), 3458–3463. New York: IEEE.
Ali, Y., M. Irfan, S. Ahmed, and S. Ahmed. 2017. “Empirical correlation of permanent deformation tests for evaluating the rutting response of conventional asphaltic concrete mixtures.” J. Mater. Civ. Eng. 29 (8): 04017059. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001888.
Ali, Y., M. Irfan, S. Ahmed, S. Khanzada, and T. Mahmood. 2015. “Sensitivity analysis of dynamic response and fatigue behaviour of various asphalt concrete mixtures.” Fatigue Fract. Eng. Mater. Struct. 38 (10): 1181–1193. https://doi.org/10.1111/ffe.12297.
Ali, Y., M. Irfan, and E. Hussain. 2020. “The impact of data noise on permanent deformation behaviour of asphalt concrete mixtures.” Int. J. Pavement Eng. 21 (12): 1470–1481. https://doi.org/10.1080/10298436.2018.1549324.
ASTM. 2010. Standard practice for preparation of bituminous specimens using Marshall apparatus. ASTM D6926. West Conshohocken, PA: ASTM.
Biligiri, K. P. 2013. “Effect of pavement materials’ damping properties on tyre/road noise characteristics.” Constr. Build. Mater. 49 (Dec): 223–232. https://doi.org/10.1016/j.conbuildmat.2013.08.016.
Biligiri, K. P., K. Kaloush, and J. Uzan. 2010. “Evaluation of asphalt mixtures’ viscoelastic properties using phase angle relationships.” Int. J. Pavement Eng. 11 (2): 143–152. https://doi.org/10.1080/10298430903033354.
Biligiri, K. P., and K. E. Kaloush. 2014. “Effect of specimen geometries on asphalt mixtures’ phase angle characteristics.” Constr. Build. Mater. 67 (Sep): 249–257. https://doi.org/10.1016/j.conbuildmat.2014.03.024.
Choi, S., and M. Do. 2020. “Development of the road pavement deterioration model based on the deep learning method.” Electronics 9 (1): 3. https://doi.org/10.3390/electronics9010003.
Connor, J. T., R. D. Martin, and L. E. Atlas. 1994. “Recurrent neural networks and robust time series prediction.” IEEE Trans. Neural Networks 5 (2): 240–254. https://doi.org/10.1109/72.279188.
Hussan, S., M. A. Kamal, I. Hafeez, and N. Ahmad. 2019. “Comparing and correlating various laboratory rutting performance tests.” Int. J. Pavement Eng. 20 (10): 1239–1249. https://doi.org/10.1080/10298436.2017.1402591.
Irfan, M., A. S. Waraich, S. Ahmed, and Y. Ali. 2016. “Characterization of various plant-produced asphalt concrete mixtures using dynamic modulus test.” Adv. Mater. Sci. Eng. 2016: 1–12. https://doi.org/10.1155/2016/5618427.
Lee, Y., J. Sun, and M. Lee. 2019. “Development of deep learning based deterioration prediction model for the maintenance planning of highway pavement.” Korean J. Constr. Eng. Manage. 20 (6): 34–43. https://doi.org/10.6106/KJCEM.2019.20.6.034.
Naik, A. K., and K. P. Biligiri. 2015. “Predictive models to estimate phase angle of asphalt mixtures.” J. Mater. Civ. Eng. 27 (8): 04014235. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001197.
Nemati, R., and E. V. Dave. 2018. “Nominal property based predictive models for asphalt mixture complex modulus (dynamic modulus and phase angle).” Constr. Build. Mater. 158 (Jan): 308–319. https://doi.org/10.1016/j.conbuildmat.2017.09.144.
NHA (National Highway Authority). 2015. General Specification. Revised specification for improvement of asphalt mixture design in Pakistan: NHA.
Nobakht, M., and M. S. Sakhaeifar. 2018. “Dynamic modulus and phase angle prediction of laboratory aged asphalt mixtures.” Constr. Build. Mater. 190 (Nov): 740–751. https://doi.org/10.1016/j.conbuildmat.2018.09.160.
Okuda, T., K. Suzuki, and N. Kohtake. 2017. “Proposal and evaluation of prediction of pavement rutting depth by recurrent neural network.” In Proc., 2017 6th IIAI Int. Congress on Advanced Applied Informatics (IIAI-AAI), 1053–1054. New York: IEEE.
Rahman, A., and R. A. Tarefder. 2018. Artificial neural network–based model to predict the complex modulus and phase angle of asphalt concrete. Washington DC: National Academy of Science.
Tabatabaee, N., M. Ziyadi, and Y. Shafahi. 2013. “Two-stage support vector classifier and recurrent neural network predictor for pavement performance modeling.” J. Infrastruct. Syst. 19 (3): 266–274. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000132.
Tarefder, R. A., L. White, and M. Zaman. 2005. “Neural network model for asphalt concrete permeability.” J. Mater. Civ. Eng. 17 (1): 19–27. https://doi.org/10.1061/(ASCE)0899-1561(2005)17:1(19).
Zhang, A., K. C. Wang, Y. Fei, Y. Liu, C. Chen, G. Yang, J. Q. Li, E. Yang, and S. Qiu. 2019. “Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network.” Comput.-Aided Civ. Infrastruct. Eng. 34 (3): 213–229. https://doi.org/10.1111/mice.12409.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Oct 19, 2020
Accepted: Jan 21, 2021
Published online: Jun 16, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 16, 2021
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.