توسعه مدل پیش‌بینی مدول برجهندگی خاک‌های رسی تثبیت شده با استفاده از شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار‌، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران

2 دانشیار، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران

3 دانشیار، بخش مهندسی عمران، دانشکدۀ فنی- مهندسی، دانشگاه شهید باهنر کرمان، ایران

چکیده

مدول برجهندگی خاک یکی از شاخص‎های مهم در طراحی روسازی راه‎هاست که نقش بسیار مهمی در تعیین ضخامت روسازی دارد. تعیین مدول برجهندگی خاک‎ها به روش مستقیم و بر اساس نتایج آزمایشگاهی به دلیل هزینه‎های بالای آن از جهت تجهیزات و نیروی انسانی معمولاً مقرون به صرفه نیست. لذا بر پایه اطلاعات میدانی گذشته می‎توان بر اساس روش‎های هوش مصنوعی اقدام به پیش‎بینی و تعیین این شاخص بر اساس داده‎های ورودی نمود. هدف از این مقاله توسعه مدلی جهت پیش‎بینی مدول برجهندگی خاک‎های رسی تثبیت شده با استفاده از شبکه عصبی مصنوعی است. بدین منظور 4 نمونه خاک مختلف تثبیت­شده با افزودنی‎هایی نظیر آهک، خاکستر بادی و غبار کوره سیمان مورد بررسی قرار گرفتند. در این مقاله از داده‎های گزارش شده در پیوست طراحی روسازی به روش آشتو 2002 استفاده شد. با مقایسه خروجی‎های بدست آمده با داده‎های واقعی بر اساس شاخص‎های آماری همچون ضریب رگرسیون و جذر میانگین مربعات خطا مشخص شد که در همه موارد نتایج مطلوبی بدست آمد. مقدار ضریب رگرسیون 99/0 و جذر میانگین مربعات خطا کمتر از 6 درصد نشان از دقت بالای مدل توسعه داده شده در پیش‎بینی مدول برجهندگی خاک‎های تثبیت شده دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Development of a Model for Predicting the Resilient Modulus of Stabilized Clay Soils Using Artificial Neural Network

نویسندگان [English]

  • Vahid Khalifeh 1
  • Ali Reza Ghanizadeh 2
  • Navid Nadimi 3
1 Assistant Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
2 Associate Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
3 Associate Professor, Civil Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
چکیده [English]

In road pavement design, soil resilient modulus is one of the factors that play a very important role in determining pavement thickness. In the past, determining the Resilient Modulus of soil directly from laboratory results has not always been practical due to its high costs, both in terms of equipment and labor. Therefore, it is possible to predict and determine this parameter based on past field data using artificial intelligence. Our goal in this paper is to develop a model for predicting the resilient modulus of stabilized clay soils using artificial neural networks. In this study, four different soil samples stabilized with additives, such as lime, fly ash, and cement kiln dust, were examined using the pavement design appendix of the AASHTO 2002 specification. By comparing the results to the laboratory data based on the statistical indicators such as regression coefficient (0.99), root mean square error (less than 6 percent), we found that the artificial neural network was highly accurate in predicting the resilient modulus.
.

کلیدواژه‌ها [English]

  • Resilience Modulus
  • Artificial Neural Network
  • Prediction
  • Pavement

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