پیش‌بینی مقاومت فشاری و کششی بستر رسی تثبیت‌شده با سیمان و باطله سنگ‌آهن با استفاده از روش‌های هوش محاسباتی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان.

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

10.22091/cer.2020.5950.1213

چکیده

یکی از روش‌های بهسازی خاک بستر روسازی، استفاده از مواد افزودنی جهت تثبیت خاک است. برای ارزیابی کیفیت مصالح از آزمایش‌های مقاومت فشاری تک‌محوری و مقاومت کششی غیرمستقیم استفاده می‌شود. بهره‌گیری از این آزمایش‌ها با توجه به زمان‌بر بودن عمل‌آوری نمونه‌ها وقت‌گیر است .همچنین در صورت افزایش تعداد نمونه‌ها می‌تواند هزینه‌بر نیز باشد. در این تحقیق، از دو روش مدل‌سازی شبکه عصبی مصنوعی (ANN) و سیستم استنتاج عصبی- فازی تطبیقی (ANFIS) جهت پیش‌بینی مقاومت فشاری و کششی خاک رس تثبیت‌شده با سیمان و باطله سنگ‌آهن استفاده شده است. برای این منظور، پارامترهای درصد سیمان، درصد باطله، رطوبت بهینه و مدت زمان عمل‌آوری، به عنوان ورودی و پارامترهای مقاومت فشاری تک‌محوری و مقاومت کششی غیرمستقیم، به عنوان خروجی در نظر گرفته شده است و در هر مورد پایگاه داده‌ای متشکل از 100 داده مورد استفاده قرار گرفته است. مدل‌سازی با استفاده از این سه روش، برتری مدل شبکه عصبی مصنوعی نسبت به سیستم استنتاج عصبی- فازی تطبیقی را نشان می‌دهد. همچنین تحلیل حساسیت نشان می‌دهد که پارامترهای درصد سیمان و درصد باطله به‌ترتیب بیشترین و کمترین تأثیر را بر مقاومت فشاری و مقاومت کششی پیش‌بینی شده دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Compressive and Tensile Strength of Clayey Subgrade Soil Stabilized With Portland Cement and Iron Ore Mine Tailing Using Computational Intelligence Methods

نویسندگان [English]

  • Toba Heidari Dezfuli 1
  • Ali Reza Ghanizadeh 2
1 Department of Civil Engineering, Sirjan University of Technology
2 Associate Professor at Faculty of Civil and Environmental Engineering, Sirjan University of Technology, Sirjan, Iran.
چکیده [English]

One of the Practical solutions for improving subgrade soil is the utilization of additives for soil stabilization. Generally, the unconfined compressive strength (UCS) and indirect tensile strength (ITS) tests are employed for quality control of stabilized materials. These tests are time- consuming due to the time needs for curing of samples, and can also be costly if the number of samples increases. In this study, we have employed two methods including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict UCS and ITS of clayey subgrade soil stabilized with Portland cement and iron ore mine tailing (IOMT). To this end, cement content, IOMT content, optimum moisture, and curing time were considered as input parameters, and unconfined compressive strength, as well as indirect tensile strength, were considered as output parameters and in each case a dataset consisting of 100 data points were used for developing computational intelligence models. Modeling by means of these three methods confirmsthe superiority of the artificial neural network model over ANFIS model. Also, the sensitivity analysis showed that the Portland cement content and IOMT Content have the greatest and lowest effect on the predicted compressive and tensile strength, respectively.

کلیدواژه‌ها [English]

  • Unconfined Compressive Strength
  • Indirect Tensile Strength
  • clay Soil
  • Portland cement and iron ore mine tailings
  • computational intelligence
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