A Novel Approach for Advancing Asphalt Pavement Temperature and Flow Number Predictions Using Optical Microscope Algorithm–Least Square Moment Balanced Machine
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
Asphalt pavement performance is crucial for the sustainable management of road infrastructure. However, achieving accurate predictions remains challenging due to the complex interactions among materials, environmental factors, and traffic loads. In this study, the optical microscope algorithm–least squares moment balanced machine (OMA-LSMBM), an AI-based inference engine, was developed to enhance the accuracy of asphalt performance prediction. This approach integrates machine-learning techniques with optimization algorithms. In the proposed model, LSMBM considers moments to determine the optimal hyperplane, a backpropagation neural network assigns weights to each datapoint, and an OMA optimizes the LSMBM hyperparameters and identifies the optimal feature subset combination. The proposed model was tested using three simulations, i.e., benchmark functions, pavement surface temperature, and asphalt mixture flow number. OMA-LSMBM demonstrated the best function approximation performance, improving the performance metrics and achieving a root mean square error value for pavement temperature prediction that was 6.49%–72.62% less than the comparison models. In terms of predicting flow number, the proposed model showed superior performance over the comparison models with a 11.15%–54.83% lower error rate. These results demonstrate the OMA-LSMBM significantly enhances the accuracy of asphalt performance predictions, which may be directly applied to improving road maintenance strategies and planning activities.
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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.
References
Abo-Hashema, M. A. 2013. “Modeling pavement temperature prediction using artificial neural networks.” In Proc., Airfield and Highway Pavement 2013: Sustainable and Efficient Pavements, 490–505. Reston, VA: ASCE.
Alavi, A. H., M. Ameri, A. H. Gandomi, and M. R. Mirzahosseini. 2011. “Formulation of flow number of asphalt mixes using a hybrid computational method.” Constr. Build. Mater. 25 (3): 1338–1355. https://doi.org/10.1016/j.conbuildmat.2010.09.010.
Barros, R., H. Yasarer, W. Uddin, and S. Sultana. 2021. “Roughness modeling for composite pavements using machine learning.” IOP Conf. Ser.: Mater. Sci. Eng. 1203 (3): 032035. https://doi.org/10.1088/1757-899X/1203/3/032035.
Bilal, B., M. Pant, H. Zaheer, L. Garcia-Hernandez, and A. Abraham. 2020. “Differential evolution: A review of more than two decades of research.” Eng. Appl. Artif. Intell. 90 (Apr): 103479. https://doi.org/10.1016/j.engappai.2020.103479.
Chang, C. C., and C. J. Lin. 2011. “LIBSVM: A library for support vector machines.” ACM Trans. Intell. Syst. Technol. 2 (3): 1–27. https://doi.org/10.1145/1961189.1961199.
Cheng, M.-Y., M.-T. Cao, and N.-M. Dao-Thi. 2023. “A novel artificial intelligence-aided system to mine historical high-performance concrete data for optimizing mixture design.” Expert Syst. Appl. 212 (Feb): 118605. https://doi.org/10.1016/j.eswa.2022.118605.
Cheng, M.-Y., M.-T. Cao, and P.-K. Tsai. 2021. “Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: A Taiwan case study.” J. Comput. Des. Eng. 8 (1): 268–282. https://doi.org/10.1093/jcde/qwaa077.
Cheng, M.-Y., Y.-H. Chang, and D. Korir. 2019a. “Novel approach to estimating schedule to completion in construction projects using sequence and nonsequence learning.” J. Constr. Eng. Manage. 145 (11): 04019072. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001697.
Cheng, M.-Y., Y.-F. Chiu, C.-K. Chiu, D. Prayogo, Y.-W. Wu, Z.-L. Hsu, and C.-H. Lin. 2019b. “Risk-based maintenance strategy for deteriorating bridges using a hybrid computational intelligence technique: A case study.” Struct. Infrastruct. Eng. 15 (3): 334–350. https://doi.org/10.1080/15732479.2018.1547767.
Cheng, M.-Y., and R. A. Gosno. 2021. “Symbiotic polyhedron operation tree (SPOT) for elastic modulus formulation of recycled aggregate concrete.” Eng. Comput. 37 (4): 3205–3220. https://doi.org/10.1007/s00366-020-00988-y.
Cheng, M.-Y., N.-D. Hoang, L. Limanto, and Y.-W. Wu. 2014. “A novel hybrid intelligent approach for contractor default status prediction.” Knowledge-Based Syst. 71 (Nov): 314–321. https://doi.org/10.1016/j.knosys.2014.08.009.
Cheng, M.-Y., and D. Prayogo. 2014. “Symbiotic organisms search: A new metaheuristic optimization algorithm.” Comput. Struct. 139 (Jul): 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007.
Cheng, M.-Y., D. Prayogo, and Y.-W. Wu. 2019c. “Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression.” Neural Comput. Appl. 31 (Oct): 6261–6273. https://doi.org/10.1007/s00521-018-3426-0.
Cheng, M.-Y., and M. N. Sholeh. 2023. “Optical microscope algorithm: A new metaheuristic inspired by microscope magnification for solving engineering optimization problems.” Knowledge-Based Syst. 279 (Nov): 110939. https://doi.org/10.1016/j.knosys.2023.110939.
Cheng, M.-Y., H.-H. Wei, Y.-W. Wu, H.-M. Chen, and C.-W. Wu. 2018. “Optimization of life-cycle cost of retrofitting school buildings under seismic risk using evolutionary support vector machine.” Technol. Econ. Dev. Econ. 24 (2): 812–824. https://doi.org/10.3846/tede.2018.247.
Chou, J.-S., and D.-N. Truong. 2021. “A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean.” Appl. Math. Comput. 389 (Jan): 125535. https://doi.org/10.1016/j.amc.2020.125535.
Gandomi, A. H., A. H. Alavi, M. R. Mirzahosseini, and F. M. Nejad. 2011. “Nonlinear genetic-based models for prediction of flow number of asphalt mixtures.” J. Mater. Civ. Eng. 23 (3): 248–263. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000154.
Ghalandari, T., L. Shi, F. Sadeghi-Khanegah, W. Van den Bergh, and C. Vuye. 2023. “Utilizing artificial neural networks to predict the asphalt pavement profile temperature in western Europe.” Case Stud. Constr. Mater. 18 (Jul): e02130. https://doi.org/10.1016/j.cscm.2023.e02130.
Ishigami, H., T. Fukuda, T. Shibata, and F. Arai. 1995. “Structure optimization of fuzzy neural network by genetic algorithm.” Fuzzy Sets Syst. 71 (3): 257–264. https://doi.org/10.1016/0165-0114(94)00283-D.
Jamil, M., and X.-S. Yang. 2013. “A literature survey of benchmark functions for global optimisation problems.” Int. J. Math. Modell. Numer. Optim. 4 (2): 150–194. https://doi.org/10.1504/IJMMNO.2013.055204.
Karaboga, D., B. Gorkemli, C. Ozturk, and N. Karaboga. 2014. “A comprehensive survey: Artificial bee colony (ABC) algorithm and applications.” Artif. Intell. Rev. 42 (Jun): 21–57. https://doi.org/10.1007/s10462-012-9328-0.
Konak, A., D. W. Coit, and A. E. Smith. 2006. “Multi-objective optimization using genetic algorithms: A tutorial.” Reliab. Eng. Syst. Saf. 91 (9): 992–1007. https://doi.org/10.1016/j.ress.2005.11.018.
Li, N., H. Zhan, X. Yu, W. Tang, H. Yu, and F. Dong. 2021. “Research on the high temperature performance of asphalt pavement based on field cores with different rutting development levels.” Mater. Struct. 54 (2): 1–12. https://doi.org/10.1617/s11527-021-01672-3.
Li, X., W. Lin, and B. Guan. 2023. “The impact of computing and machine learning on complex problem-solving.” Eng. Rep. 5 (6): e12702. https://doi.org/10.1002/eng2.12702.
Li, Y., L. Liu, and L. Sun. 2018. “Temperature predictions for asphalt pavement with thick asphalt layer.” Constr. Build. Mater. 160 (Jan): 802–809. https://doi.org/10.1016/j.conbuildmat.2017.12.145.
Ma, Y., P. Polaczyk, M. Zhang, R. Xiao, X. Jiang, and B. Huang. 2023. “Comparative study of pavement rehabilitation using hot in-place recycling and hot-mix asphalt: Performance evaluation, pavement life prediction, and life cycle cost analysis.” Transp. Res. Rec. 2677 (1): 420–431. https://doi.org/10.1177/03611981221099907.
Madeh Piryonesi, S., and T. E. El-Diraby. 2021. “Using machine learning to examine impact of type of performance indicator on flexible pavement deterioration modeling.” J. Infrastruct. Syst. 27 (2): 04021005. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000602.
Marini, F., and B. Walczak. 2015. “Particle swarm optimization (PSO). A tutorial.” Chemom. Intell. Lab. Syst. 149 (Dec): 153–165. https://doi.org/10.1016/j.chemolab.2015.08.020.
Mirzahosseini, M., Y. M. Najjar, A. H. Alavi, and A. H. Gandomi. 2015. “Next-generation models for evaluation of the flow number of asphalt mixtures.” Int. J. Geomech. 15 (6): 04015009. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000483.
Mirzahosseini, M. R., A. Aghaeifar, A. H. Alavi, A. H. Gandomi, and R. Seyednour. 2011. “Permanent deformation analysis of asphalt mixtures using soft computing techniques.” Expert Syst. Appl. 38 (5): 6081–6100. https://doi.org/10.1016/j.eswa.2010.11.002.
Naseri, H., M. Shokoohi, H. Jahanbakhsh, M. M. Karimi, and E. O. D. Waygood. 2023. “Novel soft-computing approach to better predict flexible pavement roughness.” Transp. Res. Rec. 2677 (10): 246–259. https://doi.org/10.1177/03611981231161051.
Sadat Hosseini, A., P. Hajikarimi, M. Gandomi, F. Moghadas Nejad, and A. H. Gandomi. 2021. “Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders.” Constr. Build. Mater. 299 (Sep): 124264. https://doi.org/10.1016/j.conbuildmat.2021.124264.
Sudarsanan, N., and Y. R. Kim. 2022. “A critical review of the fatigue life prediction of asphalt mixtures and pavements.” J. Traffic Transp. Eng. 9 (5): 808–835. https://doi.org/10.1016/j.jtte.2022.05.003.
Suykens, J. A. K., and J. Vandewalle. 1999. “Chaos control using least-squares support vector machines.” Int. J. Circuit Theory Appl. 27 (6): 605–615. https://doi.org/10.1002/(SICI)1097-007X(199911/12)27:6%3C605::AID-CTA86%3E3.0.CO;2-Z.
Xu, B., H. C. Dan, and L. Li. 2017. “Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network.” Appl. Therm. Eng. 120 (Jun): 568–580. https://doi.org/10.1016/j.applthermaleng.2017.04.024.
Xue, X., M. Yao, and Z. Wu. 2018. “A novel ensemble-based wrapper method for feature selection using extreme learning machine and genetic algorithm.” Knowl. Inf. Syst. 57 (2): 389–412. https://doi.org/10.1007/s10115-017-1131-4.
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© 2024 American Society of Civil Engineers.
History
Received: Jan 4, 2024
Accepted: May 24, 2024
Published online: Aug 8, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 8, 2025
ASCE Technical Topics:
- Algorithms
- Asphalt pavements
- Design (by type)
- Engineering fundamentals
- Engineering mechanics
- Errors (statistics)
- Highway and road management
- Highway transportation
- Infrastructure
- Load factors
- Material mechanics
- Material properties
- Materials engineering
- Mathematics
- Measurement (by type)
- Models (by type)
- Optimization models
- Pavements
- Statistics
- Structural design
- Structural engineering
- Temperature (by type)
- Temperature effects
- Temperature measurement
- Thermal properties
- Thermodynamics
- Transportation engineering
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