Performance of Soft Computing Technique in Predicting the Pavement International Roughness Index: Case Study
Tóm tắt
Từ khóa
Tài liệu tham khảo
Suman, S. K., & Sinha, S. (2012). Pavement condition forecasting through artificial neural network modelling. International Journal of Emerging Technology and Advanced Engineering, 2(11), 474–478.
Li, S. E., & Peng, H. (2012). Strategies to minimize the fuel consumption of passenger cars during car-following scenarios. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226(3), 419–429.
Ozbay, K., & Laub, R. (2001). Models for pavement deterioration using LTPD. FHWA-NJ-1999-030. Washington: Federal Highway Administration.
Ziari, H., Sobhani, J., Ayoubinejad, J., & Hartmann, T. (2016). Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. International Journal of Pavement Engineering, 17(9), 776–788.
Islam, S., Buttlar, W. G., Aldunate, R. G., & Vavrik, W. R. (2014). Measurement of pavement roughness using android-based smartphone application. Transportation Research Record, 2457(1), 30–38.
Guisan, et al. (2000). Effect of boundary layer conductance on the response of stomata to humidity. Plant, Cell & Environment, 8(1), 55–57.
Attoh-Okine, N. O., Mensah, S., & Nawaiseh, M. (2003). A new technique for using multivariate adaptive regression splines (MARS) in pavement roughness prediction. Proceedings of the Institute of Civil Engineers-Transport, 156(1), 51–56.
Choi, J. H., Adams, T. M., & Bahia, H. U. (2004). Pavement roughness modeling using back-propagation neural networks. Computer-Aided Civil and Infrastructure Engineering, 19(4), 295–303.
Wang, K., Li, Q., Hall, K. D., et al. (2007). Experimentation with gray theory for pavement smoothness prediction. Transportation Research Record, 1990, 3–13.
Owolabi, A. O., Sadiq, O. M., & Abiola, O. S. (2012). Development of performance models for a typical flexible road pavement in Nigeria. International Journal for Traffic and Transport Engineering, 2(3), 178–184.
Khattak, M. J., Nur, M. A., Bhuyan, M. R. U. K., & Gaspard, K. (2014). International roughness index models for HMA overlay treatment of flexible and composite pavements. International Journal of Pavement Engineering, 15(4), 334–344.
Mubaraki, M. (2016). Highway subsurface assessment using pavement surface distress and roughness data. International Journal of Pavement Research and Technology, 9(5), 393–402.
Graupe, D. (2013). Advanced series in circuits and systems: Volume 7 principles of artificial neural networks. World Scientific Publishing Co. Ptc. Ltd.
Chen, C. T., Hung, C. T., Chou, C. C., Chiang, Z., & Lin, J. D. (2008). The predicted model of international roughness index for drainage asphalt pavement. In: International Conference on Intelligent Computing (pp. 937–945). Springer, Berlin, Heidelberg.
Hoang, N. D., & Nguyen, Q. L. (2018). Automatic recognition of asphalt pavement cracks based on image processing and machine learning approaches: a comparative study on classifier performance. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/6290498
Hoang, N. D., & Nguyen, Q. L. (2019). A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering with Computers, 35(2), 487–498.
Nabipour, N., Karballaeezadeh, N., Dineva, A., Mosavi, A., Mohammadzadeh, S. D., & Shamshirband, S. (2019). Comparative analysis of machine learning models for prediction of remaining service life of flexible pavement. Mathematics, 7(12), 1198.
Ceylan, H., Bayrak, M. B., & Gopalakrishnan, K. (2014). Neural networks applications in pavement engineering: A recent survey. International Journal of Pavement Research & Technology, 7(6), 434–444
Terzi, S. (2007). Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction and Building Materials, 21(3), 590–593.
Kirbasß, U., & Karasßahin, M. (2016). Performance models for hot mix asphalt pavements in rural roads. Construction and Building Materials, 116, 281–288.
Plati, C., Georgiou, P., & Papavasiliou, V. (2016). Simulating pavement structural condition using artificial neural networks. Structure and Infrastructure Engineering, 12(9), 1127–1136.
Amadore, A., Bosurgi, G., & Pellegrino, O. (2013). Identification of the most important factors in the compaction process. Journal of Civil Engineering and Management, 19(sup1), S116–S124.
Bosurgi, G., D’Andrea, A., & Pellegrino, O. (2013). What variables affect to a greater extent the driver’s vision while driving? Transport, 28(4), 331–340.
Woldemariam, W., Murillo-Hoyos, J., & Labi, S. (2016). Estimating annual maintenance expenditures for infrastructure: Artificial neural network approach. Journal of Infrastructure Systems, 22(2), 04015025.
Xiao, F., Amirkhanian, S. N., Juang, C. H., Hu, S., & Shen, J. (2012). Model developments of long-term aged asphalt binders. Construction and Building Materials, 37, 248–256.
Amin, M. S. R., & Amador-Jiménez, L. E. (2016). Pavement management with dynamic traffic and artificial neural network: A case study of Montreal. Canadian Journal of Civil Engineering, 43(3), 241–251.
Marcelino, P., de Lurdes Antunes, M., Fortunato, E., & Gomes, M.C. (2019). Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22, 341–354.
Vyas, V., Singh, A. P., & Srivastava, A. (2021). Prediction of asphalt pavement condition using FWD deflection basin parameters and artificial neural networks. Road Materials and Pavement Design, 22(12), 2748–2766.
Nitsche, P., Stütz, R., Kammer, M., & Maurer, P. (2014). Comparison of machine learning methods for evaluating pavement roughness based on vehicle response. Journal of Computing in Civil Engineering, 28(4), 04014015.
Younos, M. A., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). Multi-input performance prediction models for flexible pavements using LTPP database. Innov Infrast Solut, 5(1), 1–11.
Inkoom, S., Sobanjo, J., Barbu, A., & Niu, X. (2019). Pavement crack rating using machine learning frameworks: Partitioning, bootstrap forest, boosted trees, Naïve bayes, and K-Nearest neighbors. Journal of Transportation Engineering, Part B: Pavements, 145(3), 04019031.
Zhao, F., Di, S., Cao, J., & Tang, J. (2021). A novel cooperative multi-stage hyper-heuristic for combination optimization problems. Complex System Modeling and Simulation, 1(2), 91–108.
Zhao, F., Ma, R., & Wang, L. (2021). A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system. IEEE Transactions on Cybernetics, 52(12), 12675–12686.
Huang, Y., & Moore, R. K. (1997). Roughness level probability prediction using artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1592, 89–97.
Demuth, H., & Beale, M. (1992). Neural network toolbox for use with MATLAB: User’s guide. Mathworks, Natick, Mass.
Chang Albitres, C., Krugler, P., and Smith, R. (2005). A Knowledge Approach Oriented to Improved Strategic Decisions in Pavement Management Practices, in 1st Annual Inter-university Symposium of Infrastructure Management, Waterloo, Ontario, Canada.
Moazami, D., & Behbahani, H. (2011). Pavement Rehabilitation and maintenance prioritization of urban roads. Using Fuzzy Logic, in Expert Systems with Applications, 38(10), 12869–12879.
Jang, J., Sun, C., & Mizutani, E. (1997). Mizutani neuro-fuzzy and soft computing. Prentice Hall.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications, 77(4), 4379–4399.
Ali, A., Dhasmana, H., Hossain, K., & Hussein, A. (2021). Modeling pavement performance indices in harsh climate regions. Journal of Transportation Engineering, Part B: Pavements, 147(4), 04021049.
Ali, A., Hossain, K., Hussein, A., Swarna, S., Dhasmana, H., & Hossain, M. (2019). Towards development of PCI and IRI models for road networks in the City of St. John’s. In: Airfield and highway pavements 2019: Design, construction, condition evaluation, and management of pavements (pp. 335–342). Reston, VA: American Society of Civil Engineers.