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پیشبینی دادهمحور خواص رئولوژیک مخلوطهای آسفالتی با استفاده از الگوریتم KNN | ||
| مهندسی زیر ساخت های حمل و نقل | ||
| دوره 11، شماره 3 - شماره پیاپی 43، آبان 1404، صفحه 117-136 اصل مقاله (1.37 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jtie.2025.39733.1746 | ||
| نویسندگان | ||
| مجتبی خدادادی* 1؛ محمد اسماعیلی2؛ حسین پهلوانی فرد2؛ محمدحسین شیرمحمدی2؛ علیرضا رشیدی نژاد2؛ محمود قلیچ خان2؛ ابوالفضل خیرخواه2 | ||
| 1استادیار گروه راه و ترابری، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران. | ||
| 2شهرداری کلانشهر قم، قم، ایران. | ||
| تاریخ دریافت: 25 آبان 1404، تاریخ بازنگری: 11 آذر 1404، تاریخ پذیرش: 13 آذر 1404 | ||
| چکیده | ||
| مدول دینامیکی (E*) و زاویه فاز (φ) از پارامترهای کلیدی در توصیف رفتار ویسکوالاستیک مخلوطهای آسفالتی هستند. با این حال، ارزیابی آزمایشگاهی آنها مستلزم انجام آزمایشهای طولانی و پُرهزینه است. به منظور غلبه بر این محدودیتها، مدلهای پیشبینی تجربی متعددی ارائه شدهاند که در میان آنها مدلهای Witczak و Hirsch بیشترین پذیرش را دارند. در سالهای اخیر، روشهای یادگیری ماشین (ML) به دلیل توانایی بالای خود در تحلیل دادهها، بهینهسازی و پیشبینی، توجه زیادی در حوزههای مختلف مهندسی جلب کردهاند. در این پژوهش، الگوریتم KNN به عنوان روشی مبتنی بر یادگیری ماشین برای پیشبینی رفتار ویسکوالاستیک مخلوطهای آسفالتی معرفی شده است. مدل توسعهیافته با استفاده از یک پایگاه داده مناسب شامل ویژگیهای قیر، پارامترهای حجمی و مقادیر مدول دینامیکی و زاویه فاز در دماها و فرکانسهای مختلف، با بیش از ۵۵۰۰ نقطه داده آموزش و اعتبارسنجی شده است. نتایج نشان داد که مدل پیشنهادی KNN قادر است E* و φ را با دقت زیاد پیشبینی کند، بهطوری که برای مجموعه آزمون مقادیر R2=0.86، Se/Sy=0.33 برای مدول دینامیکی و مقادیر R2=0.78، Se/Sy=0.45 برای زاویه فاز بهدست آمد که بیانگر توان مناسب مدل در بازتولید رفتار رئولوژیک مخلوطهای آسفالتی در دامنههای مختلف دما و فرکانس است. | ||
| کلیدواژهها | ||
| یادگیری ماشین؛ خواص رئولوژیک؛ رویکرد دادهمحور؛ الگوریتم KNN؛ مخلوط آسفالتی | ||
| عنوان مقاله [English] | ||
| Data-Driven Estimation of Rheological Behavior of Asphalt Mixture Using the K-Nearest Neighbors Algorithm | ||
| نویسندگان [English] | ||
| Mojtaba Khodadadi1؛ Mohammad Esmaily2؛ Hoseein Pahlevany Fard2؛ Mohammad Hoseein Shirmohammadi2؛ Alireza Rashidinejad2؛ Mahmoud Ghelichkhan2؛ Abolfazl Kheirkhah2 | ||
| 1Assistant Professor, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, I. R. Iran. | ||
| 2Qom Municipality, Qom, I. R. Iran. | ||
| چکیده [English] | ||
| Dynamic modulus (|E*|) and phase angle (φ) are key parameters for describing the viscoelastic performance of asphalt mixtures. However, their experimental evaluation involves lengthy testing and costly laboratory procedures. To overcome these limitations, several predictive models have been introduced, among which the Witczak and Hirsch models are the most recognized. In recent years, machine learning (ML) techniques have gained attention in engineering applications due to their strong capabilities in data analysis, optimization, and prediction. This study introduces the K-Nearest Neighbors (KNN) algorithm as an ML-based method to estimate the viscoelastic behavior of asphalt mixtures. The model was trained and validated using an extensive dataset comprising bitumen characteristics, volumetric parameters, and measured values of dynamic modulus and phase angle at various temperatures and loading frequencies, totaling over 5500 data points. The results demonstrate that the proposed ML model provides high prediction accuracy and represents a promising alternative for estimating the viscoelastic properties of asphalt mixtures. | ||
| کلیدواژهها [English] | ||
| Machine Learning, Rheological characterization, Data-driven approach, KNN algorithm, Asphalt mixture | ||
| مراجع | ||
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