پیش‌بینی خرابی پلکانی ‌شدن در روسازی‌های‌ بتنی غیرمسلح درزدار و تعیین پارامترهای موثر بر این خرابی با استفاده از شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی عمران و محیط زیست، دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران)، تهران، ایران

چکیده

یکی از خرابی‌های مهم عملکردی در روسازی‌های بتنی، خرابی پلکانی‌ شدن است. پیش‌بینی مقدار این خرابی می‌تواند در طراحی بهینه روسازی بتنی و نیز استقرار سامانه مدیریت تعمیر و نگهداری روسازی­ها مورد استفاده قرار گیرد. در این مطالعه از شبکه‌های عصبی مصنوعی برای پیش‌بینی مقدار این خرابی بر اساس داده­های عملکرد طولانی­ مدت روسازی (LTPP) استفاده شده است. ابتدا با استفاده از 32 متغیر انتخابی ورودی شامل داده­ های ترافیکی، آب و هوایی و سازه­ای، معماری شبکه عصبی مصنوعی با روش آزمون و خطا تعیین شده و سپس معماری مشخص شده به درستی آموزش داده شده است. علاوه‌ بر متغیرهای مورد استفاده در مطالعات گذشته، متغیرهای ورودی جدیدی نظیر ضریب پواسون و مدول الاستیسیته دال بتنی که تاکنون بررسی نشده‌اند نیز در بین این 32 متغیر مد نظر قرار گرفته است. سپس با به کارگیری روش جدید NSGA2-MLP، 19 متغیر مهم شناسایی شده و یک مدل شبکه عصبی جدید با این تعداد متغیر ساخته شده است. مقدار ضریب همبستگی، میانگین مربعات خطا و میانگین خطای مطلق برای مدل ساخته شده با 32 متغیر و 19 متغیر به ترتیب برابر 0/97، 0/45، 0/43، 0/95، 0/54 و 0/6 می‌باشد. در انتها با استفاده از روش جنگل تصادفی میزان اهمیت 19 متغیر بر اساس درصد تعیین گردید. چهار متغیری که بیشترین اهمیت را دارند بر اساس سهم درصد اهمیت متغیر از 100 به ترتیب عبارتند از تعداد تجمعی روزهای با بارش بیشتر از 12/7 میلی‌متر با 24%، مدول الاستیسته دال بتنی با %14، عمر روسازی با 12% و ضخامت اساس با 10%  اهمیت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Faulting Prediction Model in Jointed Plain Concrete Pavement and determining the parameters affecting this failure with Artificial Neural Networks

نویسندگان [English]

  • Mehrdad Ehsani
  • Fereidoon Moghadas Nejad
  • Pouria Hajikarimi
a Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran,
چکیده [English]

Faulting is one of the most common functional failures in concrete pavements. Pavement design and pavement management systems can both benefit from predicting this failure. Therefore, predicting this failure can be very useful. Artificial neural networks, a powerful technique, were utilized in this study to predict this failure. The artificial neural network architecture was first determined by trial and error using 32 input variables such as traffic, weather, and structural data, and then the defined architecture was appropriately trained. New input factors that have not been explored before, such as Poisson's ratio and elastic modulus of concrete slabs, have been considered among these 32 variables, in addition to the variables utilized in earlier studies. After that, 19 input variables were discovered using a new method, and a new neural network model with 19 variables was created. Notably, the feature selection method used in this study has been developed using the metaheuristic optimization algorithm. For the model with 32 variables and 19 variables, the correlation coefficient, mean square error, and mean absolute error are 0.97, 0.45, 0.43, 0.95, 0.54, and 0.6, respectively. Random forest is recognized in data mining as a powerful technique for identifying the importance of input variables. Finally, the importance of 19 variables was assessed using the random forest approach, with the four most important variables being the yearly cumulative number of days with precipitation more than 12.7 mm (24%), elastic modulus (14%), pavement life (12%), and base thickness (10%). It is found that elastic modulus is an essential input factor that has not been considered in prior studies.

کلیدواژه‌ها [English]

  • Jointed Plain Concrete Pavement (JPCP)
  • Faulting Failure
  • Artificial Neural Networks (ANN)
  • Feature Selection
  • Machine Learning

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نشریه مهندسی عمران امیرکبیر
دوره 54، شماره 7
مهر 1401
صفحه 2547-2568

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