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پیشبینی خرابی شیار شدگی در روسازیهای انعطافپذیر با استفاده از شبکه عصبی مصنوعی و الگوریتم ژنتیک | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 18، دوره 54، شماره 9، آذر 1401، صفحه 3581-3602 اصل مقاله (2.54 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2022.20965.7581 | ||
| نویسندگان | ||
| علیرضا عسکری؛ پوریا حاجی کریمی* ؛ مهرداد احسانی؛ فریدون مقدس نژاد | ||
| دانشکده مهندسی عمران و محیط زیست، دانشگاه صنعتی امیرکبیر (پلیتکنیک تهران)، تهران، ایران | ||
| چکیده | ||
| شیار شدگی یکی از خرابیهای مهم عملکردی در روسازیهای آسفالتی است. توسعه مدلهای پیشبینی به منظور جلوگیری و کنترل آسیبهای ناشی از این خرابی در سیستم مدیریت روسازی ضروری است. در این مطالعه با کمک الگوریتم شبکه عصبی مصنوعی، مدلهایی برای پیشبینی مقدار خرابی شیار شدگی با استفاده از پایگاه داده برنامه بلند مدت روسازی (LTPP) توسعه داده شده است. این مدلها برای اقلیمهای آب و هوایی سرد و مرطوب، گرم و خشک و سرد و خشک ارائه شدهاند. از آنجا که دقت مناسب در عین سادگی جزء مهمترین ویژگیهای یک مدل پیشبینی به شمار میرود، با استفاده از روش بهینهسازی چندهدفه NSGA ІІ-MLP متغیرهایی که میزان اهمیت بیشتری در پیشبینی خرابی شیار شدگی دارند، مشخص و به عنوان ورودی مدل در نظر گرفته شدند. سپس با استفاده از متغیرهای ترافیکی، آب و هوایی و سازهای انتخاب شده توسط الگو ریتم ژنتیک، مدلهای پیشبینی خرابی شیار شدگی ساخته شدهاند. مقدار ضریب تعیین و میانگین مربعات خطا برای مدل ساخته شده در مناطق سرد و مرطوب و مدل مشترک مناطق گرم و خشک و سرد و خشک به ترتیب برابر 0/96، 2/05، 0/94 و3/45 میباشد. همچنین با انجام تحلیل حساسیت، متغیرهایی که بیشترین اثرگذاری را بر خرابی شیار شدگی در اقلیم سرد و مرطوب دارند، به ترتیب اهمیت سن و اختلاف دمای حداکثر و حداقل روزانه با تاثیر مستقیم و ضخامت روسازی با تاثیر معکوس مشخص گردید. همچنین در اقلیم گرم و سرد خشک به ترتیب متغیرهای بار ترافیکی و نفوذپذیری قیر با تاثیر مستقیم و ضخامت روسازی با تاثیر معکوس بر خرابی از بیشترین اهمیت برخورداراند. | ||
| کلیدواژهها | ||
| شیار شدگی؛ روسازی انعطافپذیر؛ شبکه عصبی مصنوعی؛ بهینهسازی چند هدفه؛ الگوریتم ژنتیک | ||
| موضوعات | ||
| راه و ترابری و حمل و نقل - روسازی انعطاف پذیر | ||
| عنوان مقاله [English] | ||
| Prediction of rutting deterioration in flexible pavements using artificial neural network and genetic algorithm | ||
| نویسندگان [English] | ||
| Alireza Askari؛ Pouria Hajikarimi؛ Mehrdad Ehsani؛ Fereidoon Moghadas Nejad | ||
| Amirkabir University of TechnologyDepartment of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran. | ||
| چکیده [English] | ||
| Rutting is one of the major deteriorations of asphalt pavement, significantly impacts road safety and service quality. Prediction models are necessary to prevent and control the damage caused by this deterioration in the pavement management system. In this study, using the artificial neural network algorithm, models have been developed to predict the amount of rutting deterioration using the long-term pavement performance (LTPP) database. These models have been developed for wet freeze, dry freeze, and dry no-freeze climates. Since proper accuracy and simplicity are the most important features of a prediction model, using the NSGA ІІ-MLP multi-objective optimization method, the more important variables in predicting rutting deterioration are identified and selected as the model input. Then, using traffic, climatic and structural variables selected from the genetic algorithm, rutting deterioration prediction models were developed. The coefficient of determination and the mean squared error for the model made in the wet freeze zone and the model of dry freeze and dry no freeze zones are equal to 0. 96, 2.05, 0.94 and 3.45, respectively. Also, by performing sensitivity analysis, the effect of input data of each model on rutting deterioration was determined. The cumulative maximum and minimum daily temperature difference per year, pavement age, asphalt layer thickness, annual equivalent single axle loads, and bitumen penetration are the most impactful variables that have the greatest impact on rutting deterioration. | ||
| کلیدواژهها [English] | ||
| Rutting, Flexible pavement, Artificial neural network, Multi-objective optimization, Genetic algorithm | ||
| مراجع | ||
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