Planning of airport pavement with artificial intelligence methods

Abstract

Rigid pavements slab thicknesses are determined using readings from design curves where human, reading, and curve mistakes could commonly occur. In addition, readings from these design curves take precious time and need high attention and diligence. In this study, the ANFIS model is developed instead of the traditional curve reading method, which is more practical and timesaving. So, it could decrease the mistakes which are occurring from curve readings. For this purpose, it has produced a random data set. A slab thickness for each data in the set has been determined using design curve readings. Obtained slab thicknesses are used for training the ANFIS model and an alternative method has been obtained. The created model has predicted the slab thicknesses with a regression of 97.05% compared to the slab thicknesses obtained from curve readings.

Keywords

• Airport pavements
• slab thickness
• ANFIS
• design curve
• model

References

1. Abduljabar, J.S., 2011. Using Fuzzy Logic Methods for Carbon Dioxide Control in Carbonated Beverages. Ankara University Graduate School of Natural and Applied Sciences, Master's Thesis, 94 pp, Ankara.
2. Airport Handling Manual; Part 6 Control of Obstacles, 2007. T.R. Ministry of Transport, Department of Airports, General Directorate of Civil Aviation Publications. Access Date: 08.02.2013. http://web.shgm.gov.tr/hadyayin/hadt01.pdf
3. Angelov, P.P., Filev, D.P., 2004. An Approach to Online Identification of Takagi- Sugeno Fuzzy Models. IEEE Transactıons On Systems, Man, And Cybernetıcs—Part B: Cybernetics, 34 (1), 484-498.
4. Ashford, N.J., Mumayiz, S., Wright, P.H., 2011. Airport Engineering: Planning, Design, and Development of 21st Century Airports. John Wiley and Sons. Yayınları. 754s., Canada.
5. Bingöl, G., 2000. Design and Rehabilitation Methods of Aerodrome Pavements. Istanbul Technical University Institute of Science and Technology, Master Thesis, 89pp, İstanbul
6. Cetin, O., Kurnaz, S., Kaynak, O. 2011. A Fuzzy Logic-Based Approach to The Design of Autonomous Landing System for Unmanned Aerial Vehicles. Journal of Intelligent & Robotic Systems, 61(2011), 239-250.
7. Chao, C. C., Lirn, T. C., Lin, H. C. 2017. Indicators and Evaluation Model For Analyzing Environmental Protection Performance of Airports. Journal of Air Transport Management, 63, 61-70.
8. Federal Aviation Administration, 1995. Advisory Circular, AC No: 150/5320- 6D–7.7.95, Airport Pavement Design And Evaluation, 30s.

9. Gopalakrishnan, K., Ceylan, H. (2009). Adaptive Neuro-Fuzzy Inference Systembased Backcalculation Approach to Airport Pavement Structural Analysis. In Material Design, Construction, Maintenance, and Testing of Pavements: Selected Papers from the 2009 GeoHunan International Conference (pp. 9-16).
10. Johansen, T.A., Shorten, R., and Murray-Smith, R., 2000. On the Interpretation and Identification of Dynamic Takagi–Sugeno Fuzzy Models. IEEE Transactıons On Fuzzy Systems, Vol. 8, No. 3, J297-313.
11. Kaur, A., Kaur, A. 2012. Comparison of Fuzzy Logic and Neuro-Fuzzy Algorithms for air Conditioning System. International journal of soft computing and engineering, 2(1), 417-20.
12. Kıyıldı R.K. Karaşahin M. 2008. The Capasity Analysis of the Check-in Unit of Antalya Airport Using the Fuzzy Logic Method. Transportation Research Part A 42 (2008) 610-619.
13. Kuloğlu, N., Özdemir, M.A., Kök, B.V., 2007. Evaluation of Airport Flexible Pavement Design Methods. 7th Transport Congress, 19-21 September, TMMOB Chamber of Civil Engineers Istanbul Branch, Istanbul, 205-215.
14. Lu, X. G., Liu, M., & Liu, J. X. (2017). Design and Optimization of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot Trajectory Control. International Journal of Fuzzy Systems, 19(1), 190-206.
15. Mambo A.D. Efthekhari M. Thomas S. 2012. Fuzzy Supervisory Control Strategies to Minimise Energy Use of Airport Terminal Buildings, Proceedings of the 18th International Conference on Automation & Computing, Loughborough University, Leicestershire, UK, 8 September 2012.
16. Mert, Z.G., Yılmaz, S., 2009. Evaluation of Social Infrastructure with Fuzzy Logic Approach ıin Kocaeli Neighborhood. Journal of Engineering and Architecture Faculty of Eskisehir Osmangazi University, 22(3), 167-183.
17. Nho, K., Agarwal, R. K. 2000. Automatic Landing System Design Using Fuzzy Logic. Journal of Guidance, Control, and Dynamics, 23(2000), 298-304.
18. Okur, F., 2008. Airport Pavement Design Methods. Istanbul Technical University Institute of Science and Technology, Master Thesis, 99pp, İstanbul
19. Rezaee, M. J., Yousefi, S. 2017. An Intelligent Decision Making Approach for Identifying and Analyzing Airport Risks. Journal of Air Transport Management.
20. Skorupski J. Unchronski P. 2015, FuzzyIİnference System for the Efficiency Assessment of Hold Baggage Security Control at The Airport, Safety Science 79 (2015) 314 – 323.
21. Sugeno, M. and Yasukawa, T., 1993. A Fuzzy-Logic-Based Approach to Qualitative Modeling, lEEE Transactions On Fuzzy Systems, 1(1), 7-31.
22. Tsai, S.H., and Chen, Y.W., 2018. A Novel Identification Method for TAKAGİ– Sugeno Fuzzy Model. Fuzzy Sets and Systems, Volume 338, 117-135. Tweedale, J. W. 2012. Fuzzy Control Loop in an Autonomous Landing System for Unmanned Air Vehicles. In Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on (pp. 1-8). IEEE.