تخمین ضریب فشار جانبی ماسه ها با استفاده از آزمایش نفوذ مخروط در محفظه کالیبراسیون و شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی عمران، دانشگاه صنعتی شریف، تهران، ایران

چکیده

تعیین دقیق و مناسب پارامترهای خاک همواره در طراحی‌های ژئوتکنیکی مورد توجه بوده است. پیش بینی دقیق پارامترهای تاثیرگذار ماسه از آزمایشات برجا نظیر (CPT)، یکی از چالشی‌ترین مسایل در مهندسی ژئوتکنیک است. در این تحقیق با استفاده از نتایج آزمایش کالیبراسیون نفوذ مخروط که در دانشگاه‌ها و موسسات معتبر انجام شده‌اند و همچنین سیستمی متشکل از سه نوع شبکه عصبی مصنوعی، پارامتر ضریب فشار جانبی ماسه در حالت سکون(K0) برای انواع مختلف ماسه‌های موجود در پایگاه داده جمع‌آوری شده، به طور نسبتا دقیقی پیش بینی شده است. در این سیستم مجموعه‌ای از شبکه‌های عصبی به طور سری وظایفی انجام می‌دهند و در نهایت با ترکیب مناسب این شبکه‌ها، سیستم قادر خواهد بود که پارامتر(K0) را با دقت مناسب برای خاک‌های ماسه‌ای مورد بررسی در پایگاه داده، پیش‌بینی نماید. در این روش از شبکه عصبی خودسازمانده (SOM) برای خوشه‌بندی مناسب داده‌ها، از شبکه عصبی احتمالاتی (PNN) برای کلاسه‌بندی ماسه و در نهایت از شبکه عصبی چندلایه با الگوریتم پس انتشار(BP) برای مدل نهایی، استفاده می‌گردد. جزییات ایجاد و به کارگیری چنین سیستمی در مقاله شرح داده شده و همچنین در پایان، نتایج بدست آمده از این سیستم با نتایج سایر محققین مقایسه گردیده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Determination of Coefficient of Lateral Earth Pressure at Rest for Sandy Soil Using Cone Penetration Test and Artificial Neural Network

نویسندگان [English]

  • M. M. Ahmadi
  • N. Besharat
Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
چکیده [English]

The estimation of soil parameters in geotechnical practice is always an important step. Accurate prediction of sands parameters from insitu tests such as CPT is one of the most challenging problems in geotechnical engineering. In this study, using a series of reliable CPT calibration chamber test data and a system consisting of three types of neural networks, the coefficient of lateral pressure of sandy soil at rest (K0) is predicted while it has good agreement with measured data gathered in database. In this system, a series of neural networks perform some tasks and finally by strategically combining of networks, the system will be able to predict parameter (K0) with reasonable accuracy. The proposed system uses Self Organizing Map (SOM) for clustering data into training, testing and validating sets and probabilistic neural networks for classifying of sands and back propagation neural networks for conclusive function approximation. Details on the development of such a system are described in the present paper and finally results obtained by this system are compared to the available relations suggested by other researchers.

کلیدواژه‌ها [English]

  • "Coefficient of lateral pressure of sandy soil at rest"
  • "Cone Penetration Test"
  • "Calibration Chamber"
  • "Self-Organizing Map (SOM)"
  • "Probabilistic Neural Network (PNN)"

مراجع

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نشریه مهندسی عمران امیرکبیر
دوره 49، شماره 1
خرداد 1396
صفحه 67-80

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