COMPUTER TECHNOLOGIES OF AIRFIELD RIGID PAVEMENT CLASSIFICATION RATING DETERMINING USING ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.32782/apcmj.2024.4.10Keywords:
computer technologies, pavement classification rating, aircraft classification rating, artificial intelligence, method of least squares, airfield rigid pavement.Abstract
Abstract. This paper is dedicated to the specifics of using artificial intelligence for determining the classification rating of airfield rigid pavements. Artificial intelligence (AI) is the behavior of a computer system that simulates the decision-making process of a human. The International Civil Aviation Organization (ICAO) has adopted a new method called Aircraft Classification Rating/Pavement Classification Rating (ACR/PCR), which will replace the Aircraft Classification Number Pavement Classification Number (ACN/PCN) method. ACR is the aircraft classification rating, defined as twice the permissible wheel load, expressed in hundreds of kilograms. PCR is the classification rating of airfield pavement, defined as the ACR of the “critical” or reference aircraft at its maximum permissible takeoff weignt. Due to the introduction of ICAO's new ACR/PCR method, there is a need to adapt the Ukrainian methodology to meet these new requirements. Using the ICAO ACR computer program, the ACR classification rating for various values of standard load on a four-wheel support was determined. One of the areas where AI has a significant impact is in regression analysis. The article examines the use of AI, specifically ChatGPT, for regression analysis in determining the classification rating of airfield rigid pavements. Empirical formulas for determining the airfield rigid pavement classification rating (PCR) for four categories of subgrade strength have been obtained. AI chatbots ChatGPT can be used for regression analysis, it does not provide immediate numerical answers, and further calculations require using a program developed by ChatGPT. Computer modeling technologies and artificial intelligence have the potential to improve airport construction.
References
Advisory Circular No 150/5335-D. Standardized Method of Reporting Airport Pavement Strength-PCR. U.S. Federal Aviation Administration (FAA). Department of Transportation, 2022. 102 p.
Angeliki Armeni, Andreas Loizos. Preliminary evaluation of the ACR-PCR system for reporting the bearing capacity of flexible airfield pavements. Transportation Engineering. 2022. Vol. 8. P. 2–14.
Sun J., Oh E., Chai G., Ma Z., Bell P. Comparison between ACN–PCN and ACR–PCR for rigid airport pavement with case study. Road Materials and Pavement Design. 2024. 1–13.
Armeni Angeliki, Loizos Andreas. Reporting the Bearing Capacity of Airfield Pavements Using PCR Index, 2024. NDT. 2. P. 16–31.
Ali Z. Ashtiani, Thomas Paniagua, Timothy Parsons, Greg Foderaro. Machine Learning Solutions for Top-Down Cracking Design of Airport Rigid Pavement. Final Report DOT/FAA/TC-22/44, Federal Aviation Administration William J. Hughes Technical Center, Aviation Research Division, Atlantic City, International Airport, New Jersey, 2022. 74 p.
Kaya O., Rezaei-Tarahomi A., Ceylan H.İ., Gopalakrishnan K., Kim S., Brill D.R. Neural Network–Based Multiple-Slab Response Models for Top-Down Cracking Mode in Airfield Pavement Design. Journal of Transportation Engineering, Part B: Pavements. 2018. № 144 (2). Р. 04018009. P. 1–9.
Kaya O. Development of Neural Network-Based Asphalt Mix Design Parameters Prediction Tool. Arabian Journal for Science and Engineering. 2002. 48. P. 12793–12804.
Rezaei-Tarahomi A., Kaya O., Ceylan H., Gopalakrishnan K., Kim S., Brill D.R. Sensitivity quantification of airport concrete pavement stress responses associated with top-down and bottom-up cracking. International Journal of Pavement Research and Technology. 2017. № 10. P. 410–420.
Rezaei-Tarahomi A., Kaya O., Ceylan H.İ., Kim S., Gopalakrishnan K., Brill D.R. Development of rapid three-dimensional finite-element based rigid airfield pavement foundation response and moduli prediction models. Transportation geotechnics. 2017. № 13. P. 81–91.
Jing C., Zhang J., Song, B. An innovative evaluation method for performance of in-service asphalt pavement with semi-rigid base. Construction and Building Materials. 2020. Vol. 235. 117376.
Salsilli R., Wahr C., Delgadillo R., Huerta J., Sepúlveda P. Field performance of concrete pavements with short slabs and design procedure calibrated for Chilean conditions. International Journal of Pavement Engineering. 2015. № 16. P. 363–379.
Rezaei Tarahomi A., Kaya O., Ceylan H.İ., Gopalakrishnan K., Kim S., Brill D.R. ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems. International Journal of Pavement Engineering. 2020. Vol. 23. P. 400–413.
Tarahomi A.R., Kaya O., Ceylan H., Gopalakrishnan K., Sunghwan Kim S., Brill D.R. ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems. International Journal of Pavement Engineering. 2022. № 23:2. P. 400–413.
Abambres M., Ferreira A. Application of ANN in Pavement Engineering: State-of-Art. Mechanical Engineering eJournal. 2017. P. 1–61.
Abed A., Thom N.H., Campos-Guereta I., Airey G. Improved Multi-Layer Analysis of Pavement Response Using Neural Networks to Optimize Numerical Integration. International Journal of Pavement Research and Technology. 2024. Vol. 17. P. 549–562.
Yang X., Jinchao G., Ling D., You Z., Lee V.C., Hasan M.R., Cheng X. Research and applications of artificial neural network in pavement engineering: a state-of-the-art review. Journal of Traffic and Transportation Engineering. 2021. Vol. 8. № 6. P. 1000–1021.
John Wolberg. Data Analysis Using the Method of Least Squares. Springer, Berlin, Heidelberg, 2006. 250 p.
Karpov V., Stepanchuk O., Dubyk O., Rodchenko O., Prentkovskis O. Improvement of Methodology of Calculation and Assessment of Transport and Operational Condition of Airfield Pavement (on the Example of Airport Pavements of Kyiv and Mykolaiv International Airports). TRANSBALTICA XIII: Transportation Science and Technology. TRANSBALTICA 2022. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. P. 806–823.
Rodchenko O. Computer technologies for concrete airfield pavement design. Aviation. 2017. № 21 (3). P. 111–117.
