Journal of Rehabilitation in Civil Engineerin، جلد ۹، شماره ۳، صفحات ۸۹-۱۰۰
عنوان فارسی
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عنوان انگلیسی Calculation of Equivalent Axle Load Factor Based on Artificial Intelligence
چکیده انگلیسی مقاله In most road pavements design methods, a solution is required to transform the traffic spectrum to standard axle load with using equivalent axle load factor (EALF). The EALF depends on various parameters, but in existing design methods, only the axle type (single, tandem, and tridem) and pavement structure number were considered. Also, the EALF only determined for experimental axles and axle details (i.e., axle weight, length, pressure), wheel type (single or dual wheel) plus pavement properties were overlooked which may cause inaccuracy and unusable for the new axle. This paper presented a developed model based on Artificial Neural Network (ANN) for calculation of EALFs considering axle type, axle length, contact area, pavement structure number (SN), tire pressure, speed, and final serviceability. Backpropagation architecture was selected for the model for the EALF prediction based on fatigue criteria. Finally, among all reviewed ANN configuration, a network with 7-13-1 was selected for the optimum network.
کلیدواژه‌های انگلیسی مقاله Pavement, Equivalent Axle Load Factor, Finite element, Abaqus, Artificial intelligence
نویسندگان مقاله Fazel Fasihi |
Ph.D. Candidate, Department of Civil Engineering, Payame Noor University, P.O.Box 19395-4697, Tehran, Iran

Mahmoud Reza Keymanesh |
Associate Professor, Department of Civil Engineering, Payame Noor University, P.O.Box 19395-4697, Tehran, Iran

Seyyed Ali Sahaf |
Assistant Professor, Faculty of Engineering, Ferdowsi University, P.O.Box 9177948974, Mashhad, Iran

Soheil Ghareh |
Associate Professor, Department of Civil Engineering, Payame Noor University, P.O.Box 19395-4697, Tehran, Iran

نشانی اینترنتی https://civiljournal.semnan.ac.ir/article_5052_c8e4f81c28b01e8e36b96590d6f955ea.pdf
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