Designing a Vehicle Speed Model on Critical Failure of Flexible Pavement
Document Type : Original Article
Authors
1 M.Sc., Student, Faculty of Engineering and Technology, Islamic Azad University of Birjand, Birjand, Iran.
2 Assistant Professor, Faculty of Engineering and Technology, Islamic Azad University of Birjand, Birjand, Iran
Abstract
The speed of vehicles can affect the performance and damage of the pavement, The two major failures in flexible pavements are fatigue cracks and rutting. The tensile stress under overlay and the compressive stress on the subgrade depend on various factors such as the speed and amount of passing load and contact surface pressure. In this research, machine learning algorithms have been used to predict the critical failure of flexible pavement based on vehicle speed. In the process of using neural networks, the data of neural networks first selects a series of random values as the primary weights and biases of the network which is one of its deficiencies. Therefore, Particle Swarm Optimization (PSO) algorithm is used to optimize the weight of neural networks. In comparison with optimization algorithms, PSO is simpler for implementation and can rapidly find optimal point. According to the modeling results, at low vehicle speeds, the probability of fatigue failures, rutting, and settling increases, while the lower the speed of the vehicle, the number of repetitions is reduced, and the increase in speed causes that Less damage to the pavement.
Keywords
-منهاج، م. ب. (1393). مبانی شبکههای عصبی: هوش محاسباتی. دانشگاه امیر کبیر.
-Al-Qadi, L., Osman Erman, G., Jaime, A., Hernandez, A., & Gamez, I. (2016). Quantitative Assessment of the Effect of Wide-Base Tires on Pavement Response by Finite Element Analysis. Journal of the Transportation Research Board, 2590.
-Bhandari, S., Xiaohua, L., & Feng, W. (2022). Understanding the effects of structural factors and traffic loading on flexible pavement performance. International Journal of Transportation Science and Technology.doi.org/10.1016/j.ijtst.2022.02.004
-Chompoorat, T., Likitlersuang, S., & Jongvivatsakul, P. (2018). The Performance of Controlled Low-strength Material Base Supporting a High-volume Asphalt Pavement. KSCE Journal of Civil Engineering, 22(6), 2055-2063.
-Georgouli, K., Plati, C., & Loizos, A. (2021). Autonomous vehicles wheel wander: Structural impact on flexible pavements. Journal of Traffic and Transportation Engineering (Enlish Edition), 8(3), 388-398. doi.org/10.1016/j.jtte.2021.04.002
-Hafez, M., Mousa, R., Awed, A., & El-Badawy, S. (2018). Soil Reinforcement Using Recycled Plastic Waste for Sustainable Pavements International Congress and Exhibition" Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology, Cham.
-Hanandeh, S. (2022). Introducing mathematical modeling to estimate pavement quality index of flexible pavements based on genetic algorithm and artificial neural networks. Case Studies in Construction Materials, 16, e00991. doi.org/10.1016/j.cscm.2022.e00991
-Hernandez, J., Gamez, A., Al-Qadi, I., & De Beer, M. (2014). Analytical Approach for Predicting Three-Dimensional Tire-Pavement Contact Load. Journal of the Transportation Research Board, 2456.
Huang, Y. H. (2004). Pavement Analysis and Design. Prentice-Hall Inc.
-Issa, A., Haya, S., & Mohammad, G. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 1(13), 101490.doi.org/10.1016/j.asej.2021.04.033
-Javidi, M. M., & Roshan, E. F. (2013). Speech emotion recognition by using combination of C5.0, neural network and support vector machine classification methods. Journal of mathematics and Computer Science, 6, 191 - 200.
-Keymanesh, M. R., Pirhadi, A., Mirshekarian, B., & Jafarian, Y. A. (2016). Sensitive Analysis of the speed of vehicle on the bottom stress of flexible pavement using finite element software. Civil Engineering Journal, 115, 62-74.
-Khabiri, M. M. (2010). The effect of stabilized subbase containing waste construction materials on reduction of pavement rutting depth. Electronic Journal of Geotechnical Engineering, 15, 1211-1219.
Kim, J., Sholar, G. A., & Kim, S. (2008). Determination of accurate creep compliance and relaxation modulus at a single temperature for viscoelastic solids. Journal of Materials in Civil Engineering, 20(2), 147–156.
-Pauli, K., Antti, K., & Ville, H. (2015). Effect of Tire Configuration on the Performance of a Low-Volume Road Exposed to Heavy Axle Loads. Journal of the Transportation Research Board, 2474.
-Rith, M., Kim, Y. K., & Lee, S. W. (2018). Behavior of RCC-base composite pavement for heavy duty area. Construction and Building Materials, 175, 144-151.
-Subhy, A. (2017). Advanced analytical techniques in fatigue and rutting related characterizations of modified bitumen: literature review. Construction and Building Materials, 156, 28–45.